Publications

Hoang, DT, Huynh, NV, Nguyen, DN, Hossain, E & Niyato, D 2023, Deep Reinforcement Learning for Wireless Communications and Networking, Wiley, New Jersey.
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Deep Reinforcement Learning for Wireless Communications and Networking Comprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless communication systems Deep Reinforcement Learning for Wireless Communications and Networking presents an overview of the development of DRL while providing fundamental knowledge about theories, formulation, design, learning models, algorithms and implementation of DRL together with a particular case study to practice. The book also covers diverse applications of DRL to address various problems in wireless networks, such as caching, offloading, resource sharing, and security. The authors discuss open issues by introducing some advanced DRL approaches to address emerging issues in wireless communications and networking. Covering new advanced models of DRL, e.g., deep dueling architecture and generative adversarial networks, as well as emerging problems considered in wireless networks, e.g., ambient backscatter communication, intelligent reflecting surfaces and edge intelligence, this is the first comprehensive book studying applications of DRL for wireless networks that presents the state-of-the-art research in architecture, protocol, and application design. Deep Reinforcement Learning for Wireless Communications and Networking covers specific topics such as: Deep reinforcement learning models, covering deep learning, deep reinforcement learning, and models of deep reinforcement learning Physical layer applications covering signal detection, decoding, and beamforming, power and rate control, and physical-layer security Medium access control (MAC) layer applications, covering resource allocation, channel access, and user/cell association Network layer applications, covering traffic routing, network classification, and network slicing With comprehensive coverage of an exciting and noteworthy new technology, Deep Reinforcement Learning for Wireless Communications and Networking is an essential learning resource for ...

Alam, SL & Gill, AQ 2023, 'Analyzing social interactions and conflicting goals: Australian government ecosystem context' in Digitalization and Sustainability, Edward Elgar Publishing, pp. 128-145.
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There is an increasing use of social media platforms by public agencies for active interactions with community. In particular, the Australian public agencies are the major users of Facebook pages for actively interacting with the community. While active interactivity seems useful, however, this also marks the need for understanding and addressing tension and conflicts between government and community goals for effective interactions and mutual benefits. There is limited analysis and research into the use and impact of Facebook pages on the Australian government and community expectations. This chapter aims to address this gap and reports findings from the analysis of the Australian government Facebook pages. It is anticipated that findings from this analysis can be used by agencies to identity and address conflicting goals when redesigning their social media platforms such as Facebook pages for interactions with the community.

Aljumaie, M, Chi Nguyen, H, Chu, NH, Nguyen, CT, Nguyen, DN, Thai Hoang, D & Dutkiewicz, E 2023, 'Potential Applications and Benefits of Metaverse', Wiley, pp. 17-37.
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Hoang, DT, Nguyen, DN, Nguyen, CT, Hossain, E & Niyato, D 2023, 'Prefce', pp. xxiv-xxiv.

Ma, B, Zhao, Y, Wang, X, Liu, Z, Lin, X, Wang, Z, Ni, W & Liu, RP 2023, 'Vehicle Trajectory Obfuscation and Detection' in Advanced Sciences and Technologies for Security Applications, Springer International Publishing, pp. 121-134.
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Poostchi, H & Piccardi, M 2023, '6 Persian Named Entity Recognition with Structural Prediction Methods' in Persian Computational Linguistics and NLP, De Gruyter, pp. 149-184.
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Vu, L, Nguyen, QU, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2023, 'A Novel Transfer Learning Model for Intrusion Detection Systems in IoT Networks' in Emerging Trends in Cybersecurity Applications, Springer International Publishing, Switzerland, pp. 45-65.
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Internet of Things, or IoT has been playing an important part in human’s lives. Nonetheless, the rapid growth of IoT-based services may lead to an increase in IoT-based attacks that compromise sensitive user data. IoT-based attack detection and prevention is thus essential for the development of IoT-based network technologies. However, this is a difficult task because the data transmitted between IoT devices is often complex and heterogeneous while IoT-based threats/attacks change rapidly over time. Machine learning-based threat detection methods, which are trained with old/existing labelled data, may become less effective in the future. For that, it is critical to develop effective learning models that can leverage both labelled and unlabelled data to timely adapt with the fast-varying attack methods. To this end, this chapter proposes a Deep Transfer Learning (DTL) model to build an effective IoT attack detection model from both labelled and unlabelled data collected from multiple IoT devices. The proposed solution combines the Multi-Maximum Mean Discrepancy (M2D) distance and two AutoEncoder (AE) networks named as Multi-Maximum Mean Discrepancy AutoEncoder (M2DA). The first AE is trained on labelled IoT data, while the second AE is trained on unlabelled IoT data. The purpose of the training process is to transfer the learned label information from the first AE to the second AE by the M2D distance. As a result, the second AE can efficiently classify and detect anomaly (attacks) even on unlabelled IoT network data. We further study the performance of the proposed M2DA framework using nine well-known commercial IoT datasets with different botnet families and attacks.

Zhang, JA, Ni, Z, Wu, K, Huang, X & Guo, YJ 2023, 'Perceptive Mobile Networks' in Integrated Sensing and Communications, Springer Nature Singapore, pp. 355-384.
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Alalyan, MS, Jaafari, NA, Hussain, FK & Gill, AQ 2023, 'A systematic review of blockchain adoption in education institutions', International Journal of Web and Grid Services, vol. 19, no. 2, pp. 156-184.
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Alalyan, MS, Jaafari, NA, Hussain, FK & Gill, AQ 2023, 'A systematic review of blockchain adoption in education institutions.', Int. J. Web Grid Serv., vol. 19, no. 2, pp. 156-184.
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Al-Maliki, S, Bouanani, FE, Ahmad, K, Abdallah, M, Hoang, DT, Niyato, D & Al-Fuqaha, A 2023, 'Toward Improved Reliability of Deep Learning Based Systems Through Online Relabeling of Potential Adversarial Attacks', IEEE Transactions on Reliability, vol. 72, no. 4, pp. 1367-1382.
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Deep neural networks have shown vulnerability to well-designed inputs called adversarial examples. Researchers in industry and academia have proposed many adversarial example defense techniques. However, they offer partial but not full robustness. Thus, complementing them with another layer of protection is a must, especially for mission-critical applications. This article proposes a novel online selection and relabeling algorithm (OSRA) that opportunistically utilizes a limited number of crowdsourced workers to maximize the machine learning (ML) system's robustness. The OSRA strives to use crowdsourced workers effectively by selecting the most suspicious inputs and moving them to the crowdsourced workers to be validated and corrected. As a result, the impact of adversarial examples gets reduced, and accordingly, the ML system becomes more robust. We also proposed a heuristic threshold selection method that contributes to enhancing the prediction system's reliability. We empirically validated our proposed algorithm and found that it can efficiently and optimally utilize the allocated budget for crowdsourcing. It is also effectively integrated with a state-of-the-art black box defense technique, resulting in a more robust system. Simulation results show that the OSRA can outperform a random selection algorithm by 60% and achieve comparable performance to an optimal offline selection benchmark. They also show that OSRA's performance has a positive correlation with system robustness.

Altaf, T, Wang, X, Ni, W, Liu, RP & Braun, R 2023, 'NE-GConv: A lightweight node edge graph convolutional network for intrusion detection', Computers & Security, vol. 130, pp. 103285-103285.
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Altaf, T, Wang, X, Ni, W, Yu, G, Liu, RP & Braun, R 2023, 'A new concatenated Multigraph Neural Network for IoT intrusion detection', Internet of Things, vol. 22, pp. 100818-100818.
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Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2023, 'RF-Self-Powered Sensor for Fully Autonomous Soil Moisture Sensing', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 3, pp. 1374-1387.
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Soil moisture monitoring and irrigation scheduling are essential parameters in farming efficiency. Internet-of-Things (IoT) technology is a promising solution for automating irrigation procedures and improving farming efficiency by removing human faults. In this article, a new method is introduced to measure soil moisture level along with providing energy to run a low-power transmitter as an alarm signal. A combination of a metamaterial perfect absorber (MPA) and two rectifiers that are designed at different frequencies specifies 5% and 25% soil moisture levels. The sensor monitors the soil moisture continuously without consuming energy. Once the soil moisture becomes 5% of the first rectifier starts working and provides 65 $\text{uW}$ dc output, while the second rectifier is off. Increasing the soil moisture to 25%, the second rectifier creates 100 uW dc output when the first rectifier is off. The designed structure is fabricated on RO4003 in a $4$ $\times$ $4$ array. The measurement results are provided by performing a set of different experiments. Initially, the MPA’s absorption characteristics are validated facing different polarization and incident angles. Then, the sensing capability is proven by burying the proposed sensor under sand and measuring the dc outputs of rectifiers. A strong correlation between simulation and measurement results validates the design procedure.

Bagirov, A, Seifollahi, S, Piccardi, M, Zare Borzeshi, E & Kruger, B 2023, 'SMGKM: An Efficient Incremental Algorithm for Clustering Document Collections', pp. 314-328.
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Bird, TS 2023, 'Platinum Jubilee of the IEEE Transactions on Antennas and Propagation', IEEE Transactions on Antennas and Propagation, vol. 71, no. 8, pp. 6276-6285.
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Cai, P, Li, H, Guo, Q & Huang, X 2023, 'UAMP-Based Equalization for Dual Pulse Shaping Transmission Systems', IEEE Wireless Communications Letters, vol. 12, no. 7, pp. 1164-1168.
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Chen, L, Chen, L, Ge, Z, Sun, Y & Zhu, X 2023, 'A 40-GHz Load Modulated Balanced Power Amplifier Using Unequal Power Splitter and Phase Compensation Network in 45-nm SOI CMOS', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 70, no. 8, pp. 3178-3186.
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Chen, L, Chen, L, Zhu, H, Gomez-Garcia, R & Zhu, X 2023, 'A Wideband Balanced Amplifier Using Edge-Coupled Quadrature Couplers in 0.13-μm SiGe HBT Technology', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 70, no. 2, pp. 631-641.
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Chen, L, Liu, Y, Yang, S, Hu, J & Guo, YJ 2023, 'Synthesis of Wideband Frequency-Invariant Beam Patterns for Nonuniformly Spaced Arrays by Generalized Alternating Projection Approach', IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 1099-1104.
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Chen, S-L, Song, L-Z, Karmokar, DK, Jones, B & Guo, YJ 2023, 'Wideband Fixed-Beam Single-Piece Leaky Wave Antenna With Controlled Dispersion Slope', IEEE Transactions on Antennas and Propagation, vol. 71, no. 11, pp. 8429-8440.
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Chen, X, Feng, Z, Wei, Z, Yuan, X, Zhang, P, Zhang, JA & Yang, H 2023, 'Multiple Signal Classification Based Joint Communication and Sensing System', IEEE Transactions on Wireless Communications, vol. 22, no. 10, pp. 6504-6517.
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Chen, Y, Huang, X, An, P & Wu, Q 2023, 'Enhanced Light Field Reconstruction by Combining Disparity and Texture Information in PSVs via Disparity-Guided Fusion', IEEE Transactions on Computational Imaging, vol. 9, pp. 665-677.
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Chen, Y-N, Ding, C, Zhu, H & Liu, Y 2023, 'A ±45°-Polarized Antenna System With Four Isolated Channels for In-Band Full-Duplex (IBFD)', IEEE Transactions on Antennas and Propagation, vol. 71, no. 4, pp. 3000-3010.
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Chu, NH, Hoang, DT, Nguyen, DN, Van Huynh, N & Dutkiewicz, E 2023, 'Joint Speed Control and Energy Replenishment Optimization for UAV-Assisted IoT Data Collection With Deep Reinforcement Transfer Learning', IEEE Internet of Things Journal, vol. 10, no. 7, pp. 5778-5793.
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Unmanned aerial vehicle (UAV)-assisted data collection has been emerging as a prominent application due to its flexibility, mobility, and low operational cost. However, under the dynamic and uncertainty of IoT data collection and energy replenishment processes, optimizing the performance for UAV collectors is a very challenging task. Thus, this paper introduces a novel framework that jointly optimizes the flying speed and energy replenishment for each UAV to significantly improve the overall system performance (e.g., data collection and energy usage efficiency). Specifically, we first develop a Markov decision process to help the UAV automatically and dynamically make optimal decisions under the dynamics and uncertainties of the environment. Although traditional reinforcement learning algorithms such as Q-learning and deep Q-learning can help the UAV to obtain the optimal policy, they often take a long time to converge and require high computational complexity. Therefore, it is impractical to deploy these conventional methods on UAVs with limited computing capacity and energy resource. To that end, we develop advanced transfer learning techniques that allow UAVs to “share” and “transfer” learning knowledge, thereby reducing the learning time as well as significantly improving learning quality. Extensive simulations demonstrate that our proposed solution can improve the average data collection performance of the system up to 200% and reduce the convergence time up to 50% compared with those of conventional methods.

Chu, NH, Nguyen, DN, Hoang, DT, Pham, Q-V, Phan, KT, Hwang, W-J & Dutkiewicz, E 2023, 'AI-Enabled mm-Waveform Configuration for Autonomous Vehicles With Integrated Communication and Sensing', IEEE Internet of Things Journal, vol. 10, no. 19, pp. 16727-16743.
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Integrated Communications and Sensing (ICS) has recently emerged as an enabling technology for ubiquitous sensing and IoT applications. For ICS application to Autonomous Vehicles (AVs), optimizing the waveform structure is one of the most challenging tasks due to strong influences between sensing and data communication functions. Specifically, the preamble of a data communication frame is typically leveraged for the sensing function. As such, the higher number of preambles in a Coherent Processing Interval (CPI) is, the greater sensing task’s performance is. In contrast, communication efficiency is inversely proportional to the number of preambles. Moreover, surrounding radio environments are usually dynamic with high uncertainties due to their high mobility, making the ICS’s waveform optimization problem even more challenging. To that end, this paper develops a novel ICS framework established on the Markov decision process and recent advanced techniques in deep reinforcement learning. By doing so, without requiring complete knowledge of the surrounding environment in advance, the ICS-AV can adaptively optimize its waveform structure (i.e., number of frames in the CPI) to maximize sensing and data communication performance under the surrounding environment’s dynamic and uncertainty. Extensive simulations show that our proposed approach can improve the joint communication and sensing performance up to 46.26% compared with other baseline methods.

Dinh, TQ, Nguyen, DN, Hoang, DT, Pham, TV & Dutkiewicz, E 2023, 'In-Network Computation for Large-Scale Federated Learning Over Wireless Edge Networks', IEEE Transactions on Mobile Computing, vol. 22, no. 10, pp. 5918-5932.
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Most conventional Federated Learning (FL) models are using a star network topology where all users aggregate their local models at a single server (e.g., a cloud server). That causes significant overhead in terms of both communications and computing at the server, delaying the training process, especially for large scale FL systems with straggling nodes. This paper proposes a novel edge network architecture that enables decentralizing the model aggregation process at the server, thereby significantly reducing the training delay for the whole FL network. Specifically, we design a highly-effective in-network computation framework (INC) consisting of a user scheduling mechanism, an in-network aggregation process (INA) which is designed for both primal- and primal-dual methods in distributed machine learning problems, and a network routing algorithm with theoretical performance bounds. The in-network aggregation process, which is implemented at edge nodes and cloud node, can adapt two typical methods to allow edge networks to effectively solve the distributed machine learning problems. Under the proposed INA, we then formulate a joint routing and resource optimization problem, aiming to minimize the aggregation latency. The problem turns out to be NP-hard, and thus we propose a polynomial time routing algorithm which can achieve near optimal performance with a theoretical bound. Simulation results showed that the proposed algorithm can achieve more than 99$\%$ of the optimal solution and reduce the FL training latency, up to 5.6 times w.r.t other baselines. The proposed INC framework can not only help reduce the FL training latency but also significantly decrease cloud’s traffic and computing overhead. By embedding the computing/aggregation tasks at the edge nodes and leveraging the multi-layer edge-network architecture, the INC framework can liberate FL from the star topology to enable ...

Fei, Z, Wang, X, Wu, N, Huang, J & Zhang, JA 2023, 'Air-Ground Integrated Sensing and Communications: Opportunities and Challenges', IEEE Communications Magazine, vol. 61, no. 5, pp. 55-61.
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Gill, AQ 2023, 'The digital ecosystem information framework: Insights from action design research', Journal of Information Science.
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Digital ecosystem (DE) is a dynamic configuration of informational organisms, individual and organisational actors, which interact in the digitally networked and federated environment. Traditional approaches are challenged by the need for handling information in complex DE where information flows beyond the boundary of a single actor. This article presents the informational organism-interaction centric digital ecosystem information (DEi) framework for information operations, management, and governance. The DEi framework emerged based on the insights obtained through the application of well-known thematic network analysis and abstraction, reflection and learning techniques to 15 action design research projects across nine different industry partners in Australia. The DEi framework includes 27 topics that are organised into nine key knowledge and three focus areas. The DEi framework can be used by researchers and practitioners as a resource for designing digital information capabilities as appropriate to their context.

Gong, S, Cui, L, Gu, B, Lyu, B, Hoang, DT & Niyato, D 2023, 'Hierarchical Deep Reinforcement Learning for Age-of-Information Minimization in IRS-Aided and Wireless-Powered Wireless Networks', IEEE Transactions on Wireless Communications, vol. 22, no. 11, pp. 8114-8127.
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In this paper, we focus on a wireless-powered sensor network coordinated by a multi-antenna access point (AP). Each node can generate sensing information and report the latest information to the AP using the energy harvested from the AP’s signal beamforming. We aim to minimize the average age-of-information (AoI) by adapting the nodes’ scheduling and the transmission control strategies jointly. To reduce the transmission delay, an intelligent reflecting surface (IRS) is used to enhance the channel conditions by controlling the AP’s beamforming strategy and the IRS’s phase shifting matrix. Considering dynamic data arrivals at different sensing nodes, we propose a hierarchical deep reinforcement learning (DRL) framework to for AoI minimization in two steps. The users’ transmission scheduling is firstly determined by the outer-loop DRL approach, e.g. the DQN or PPO algorithm, and then the inner-loop optimization is used to adapt either the uplink information transmission or downlink energy transfer to all nodes. A simple and efficient approximation is also proposed to reduce the inner-loop rum time overhead. Numerical results verify that the hierarchical learning framework outperforms typical baselines in terms of the average AoI and proportional fairness among different nodes.

Gong, S, Wang, M, Gu, B, Zhang, W, Hoang, DT & Niyato, D 2023, 'Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks', IEEE Transactions on Vehicular Technology, vol. 72, no. 8, pp. 10933-10948.
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In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while the UAVs' network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs' energy consumption and data queue size. Then, with the fixed network formation, the UAVs' trajectories are further optimized by using multi-agent deep reinforcement learning without knowing the GUs' traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs' flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs' trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs' cooperation in data offloading, thus improving energy efficiency and delay performance.

Guo, CA, Guo, YJ, Zhu, H, Ni, W & Yuan, J 2023, 'Optimization of Multibeam Antennas Employing Generalized Joined Coupler Matrix', IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 215-224.
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Currently, there is increasing interest in analog multibeam antennas whose beams can be flexibly steered to arbitrary directions. In a previous paper, we presented the theoretical framework for synthesizing individually steerable multiple beams using generalized joined coupler (GJC) matrices. The synthesis method was to optimize the array excitation vectors to approximate known distributions. In this article, we present a more robust optimization method to optimize the multibeams directly in order to control the half-power beamwidth, the sidelobe levels, and nulls for mitigating system interference. The effectiveness of the proposed method is demonstrated by numerical examples. We reveal how the quality of the multiple beams is inherently determined by the dimensions of the GJC matrix. Experimental results of a 3 10 Nolen-like GJC matrix are presented for the first time to validate the proposed method in realizing low sidelobe multibeams.

Helalian, H, Zhu, X & Atarodi, M 2023, 'A Multioutput and Highly Efficient GaN Distributed Power Amplifier for Compact Subarrays in Wideband Phased Array Antennas', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 11, pp. 4800-4813.
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Hieu, NQ, Hoang, DT, Niyato, D, Nguyen, DN, Kim, DI & Jamalipour, A 2023, 'Joint Power Allocation and Rate Control for Rate Splitting Multiple Access Networks With Covert Communications', IEEE Transactions on Communications, vol. 71, no. 4, pp. 2274-2287.
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Rate Splitting Multiple Access (RSMA) has recently emerged as a promising technique to enhance the transmission rate for multiple access networks. Unlike conventional multiple access schemes, RSMA requires splitting and transmitting messages at different rates. The joint optimization of the power allocation and rate control at the transmitter is challenging given the uncertainty and dynamics of the environment. Furthermore, securing transmissions in RSMA networks is a crucial problem because the messages transmitted can be easily exposed to adversaries. This work first proposes a stochastic optimization framework that allows the transmitter to adaptively adjust its power and transmission rates allocated to users, and thereby maximizing the sum-rate and fairness of the system under the presence of an adversary. We then develop a highly effective learning algorithm that can help the transmitter to find the optimal policy without requiring complete information about the environment in advance. Extensive simulations show that our proposed scheme can achieve non-saturated transmission rates at high SNR values with infinite blocklength. More significantly, our proposed scheme can achieve positive covert transmission rates in the finite blocklength regime, compared with zero-valued covert rates of a conventional multiple access scheme.

Hieu, NQ, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2023, 'When Virtual Reality Meets Rate Splitting Multiple Access: A Joint Communication and Computation Approach', IEEE Journal on Selected Areas in Communications, vol. 41, no. 5, pp. 1536-1548.
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Rate Splitting Multiple Access (RSMA) has emerged as an effective interference management scheme for applications that require high data rates. Although RSMA has shown advantages in rate enhancement and spectral efficiency, it has yet not to be ready for latency-sensitive applications such as virtual reality streaming, which is an essential building block of future 6G networks. Unlike conventional High-Definition streaming applications, streaming virtual reality applications requires not only stringent latency requirements but also the computation capability of the transmitter to quickly respond to dynamic users' demands. Thus, conventional RSMA approaches usually fail to address the challenges caused by computational demands at the transmitter, let alone the dynamic nature of the virtual reality streaming applications. To overcome the aforementioned challenges, we first formulate the virtual reality streaming problem assisted by RSMA as a joint communication and computation optimization problem. A novel multicast approach is then proposed to cluster users into different groups based on a Field-of-View metric and transmit multicast streams in a hierarchical manner. After that, we propose a deep reinforcement learning approach to obtain the solution for the optimization problem. Extensive simulations show that our framework can achieve the millisecond-latency requirement, which is much lower than other baseline schemes.

Hoang, T-D, Huang, X & Qin, P 2023, 'Gradient Descent-Based Direction-of-Arrival Estimation for Lens Antenna Array', IEEE Signal Processing Letters, vol. 30, pp. 838-842.
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In this letter, we investigate a novel optimization approach to direction-of-arrival (DoA) estimation for a lens antenna array. Inspired by a property of the sinc function and ${\ell _{2}}$-norm optimization, we develop the gradient descent-based spatial spectrum reconstruction (GD-SSR) to estimate the DoAs based on the sum signal covariance vector (SSCV). Our proposed algorithm does not require a priori knowledge of signal number and has a lower complexity compared with existing techniques while achieving a better estimation performance, even in a low-SNR regime. In addition, the proposed model does not require any pretraining process as prior learning-based methods. The simulation results show that our scheme not only outperforms other techniques but also resolves the angular ambiguity problem.

Huang, X, Mei, G & Zhang, J 2023, 'Cross-source point cloud registration: Challenges, progress and prospects', Neurocomputing, vol. 548, pp. 126383-126383.
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Huang, X, Tuyen Le, A & Guo, YJ 2023, 'Joint Analog and Digital Self-Interference Cancellation for Full Duplex Transceiver With Frequency-Dependent I/Q Imbalance', IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2364-2378.
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An effective and practical joint analog and digital self-interference cancellation (SIC) scheme without additional signalling overhead for an I/Q imbalanced full duplex transceiver is proposed in this paper. This scheme combines an I/Q imbalanced analog least mean square (ALMS) loop at the transceiver radio frequency frontend and a two-stage digital signal processing (DSP) at the digital baseband to achieve excellent SIC performance with low complexity. The steady state weighting coefficients of the I/Q imbalanced ALMS loop with periodical transmitted signal and the loop’s convergence behaviour are firstly analysed. The residual SI is then modelled as the output of a time-varying widely linear system. With a track/hold control mechanism applied to the ALMS loop, the system model for digital SIC is further presented, followed by the DSP algorithms suitable for real-time implementation. The noise enhancement in each stage digital cancellation is also analysed and formulated. Finally, simulation results are provided to verify the theoretical analyses and demonstrate the overall SIC performance.

Huang, Y, Li, Y, Jourjon, G, Seneviratne, S, Thilakarathna, K, Cheng, A, Webb, D & Xu, RYD 2023, 'Calibrated reconstruction based adversarial autoencoder model for novelty detection', Pattern Recognition Letters, vol. 169, pp. 50-57.
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Jafarizadeh, S & Veitch, D 2023, 'Robust Weighted-Average Continuous-Time Consensus With Communication Time Delay', IEEE Transactions on Cybernetics, vol. 53, no. 4, pp. 2074-2086.
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Jauregi Unanue, I, Haffari, G & Piccardi, M 2023, 'T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification', Transactions of the Association for Computational Linguistics, vol. 11, pp. 1147-1161.
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Jia, Z, Liu, Q, He, Y, Wu, Q, Liu, RP & Sun, Y 2023, 'Efficient end-to-end failure probing matrix construction in data center networks', Journal of Communications and Networks, vol. 25, no. 4, pp. 532-543.
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Kang, J, Jia, W & He, X 2023, 'Toward extracting and exploiting generalizable knowledge of deep 2D transformations in computer vision', Neurocomputing, vol. 562, pp. 126882-126882.
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Khoa, TV, Hoang, DT, Trung, NL, Nguyen, CT, Quynh, TTT, Nguyen, DN, Ha, NV & Dutkiewicz, E 2023, 'Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks', IEEE Internet of Things Journal, vol. 10, no. 10, pp. 8578-8589.
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Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. However, one of the biggest challenges for deploying FL in IoT networks is the unavailability of labeled data and dissimilarity of data features for training. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn “knowledge” from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning “knowledge” among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.

Li, F, Zheng, J, Zhang, Y-F, Jia, W, Wei, Q & He, X 2023, 'Cross-domain learning for underwater image enhancement', Signal Processing: Image Communication, vol. 110, pp. 116890-116890.
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The poor quality of underwater images has become a widely-known cause affecting the performance of the underwater development projects, including mineral exploitation, driving photography, and navigation for autonomous underwater vehicles. In recent years, deep learning-based techniques have achieved remarkable successes in image restoration and enhancement tasks. However, the limited availability of paired training data (underwater images and their corresponding clear images) and the requirement for vivid color correction remain challenging for underwater image enhancement, as almost all learning-based methods require paired data for training. In this study, instead of creating the time-consuming paired data, we explore the unsupervised training strategy. Specifically, we introduce a universal cross-domain GAN-based framework to generate high-quality images to address the dependence on paired training data. To ensure the vivid colorfulness, the color loss is designed to constrain the training process. Also, a feature fusion module (FFM) is proposed to increase the capacity of the whole model as well as the dual discriminator channel adopted in the architecture. Extensive quantitative and perceptual experiments show that our approach overcomes the limitation of paired data and obtains superior performance over the state-of-the-art on several underwater benchmarks in terms of both accuracy and model deployment.

Li, L, Guo, R, You, P, Bai, J, Qin, P-Y & Liu, Y 2023, 'Pattern-Reconfigurable Sparse Linear Array Synthesis Under Minimum Element Spacing Control by Alternating Sequential Quadratic Programming', IEEE Antennas and Wireless Propagation Letters, vol. 22, no. 6, pp. 1271-1275.
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A new method called alternating sequential quadratic programming is proposed to synthesize pattern-reconfigurable sparse linear arrays with minimum element spacing control. The method can find the common element positions and multiple sets of excitations for generating multiple reconfigurable patterns which accurately meet their given upper and lower pattern bounds. In addition, by introducing auxiliary weighting coefficients and collective excitation coefficient vectors and choosing them as optimization variables alternately, the proposed method can appropriately incorporate the minimum element spacing constraint into the pattern synthesis. Synthesized results show that the proposed method can give satisfactory reconfigurable pattern performance but save much more elements compared with some existing methods.

Li, M, Chen, S-L, Liu, Y & Guo, YJ 2023, 'Wide-Angle Beam Scanning Phased Array Antennas: A Review', IEEE Open Journal of Antennas and Propagation, vol. 4, pp. 695-712.
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Li, M, Liu, Y, Bao, Z, Chen, L, Hu, J & Guo, YJ 2023, 'Efficient Phase-Only Dual- and Multi-Beam Pattern Synthesis With Accurate Beam Direction and Power Control Employing Partitioned Iterative FFT', IEEE Transactions on Antennas and Propagation, vol. 71, no. 4, pp. 3719-3724.
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Li, X, Cui, Y, Zhang, JA, Liu, F, Zhang, D & Hanzo, L 2023, 'Integrated Human Activity Sensing and Communications', IEEE Communications Magazine, vol. 61, no. 5, pp. 90-96.
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Li, X, Peng, Y & Xu, M 2023, 'Patch-shuffle-based semi-supervised segmentation of bone computed tomography via consistent learning', Biomedical Signal Processing and Control, vol. 80, pp. 104239-104239.
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Lian, J-W, Ansari, M, Hu, P, Guo, YJ & Ding, D 2023, 'Wideband and High-Efficiency Parallel-Plate Luneburg Lens Employing All-Metal Metamaterial for Multibeam Antenna Applications', IEEE Transactions on Antennas and Propagation, vol. 71, no. 4, pp. 3193-3203.
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Lin, J-Y, Yang, Y, Wong, S-W, Li, X, Wang, L & Dutkiewicz, E 2023, 'Two-Way Waveguide Diplexer and Its Application to Diplexing In-Band Full-Duplex Antenna', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 3, pp. 1171-1179.
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A design of a diplexing in-band full-duplex (IBFD) slot antenna based on the quadruple-mode resonator (QMR) is presented for the first time. First, a two-way waveguide diplexer integration using QMR is designed. Four waveguide modes, namely, TE $_{011}$ , TE $_{101}$ , TM $_{\mathrm{210,}}$ and TM $_{120}$ , are primarily used. These four modes are modal orthogonal to each other in a single QMR. Each of the quadruple modes can be independently manipulated with low mutual interference with other modes. Taking advantage of this characteristic, a two-way waveguide diplexer integration can be implemented with four independent frequency channels. In each diplexer, the downlink channel is dominated by fundamental mode TE $_{011}$ /TE $_{101}$ , while the uplink channel is dominated by high-order mode TM $_{210}$ /TM $_{120}$ . By cascading QMRs, higher order frequency responses are achieved with expected coupling coefficient ( $K)$ and external quality factor ( $Q_{{\text{e}}})$ . Based on the proposed two-way diplexer concept, a diplexing IBFD antenna with a turnstile junction is designed by replacing the inputs with a cross-coupled radiating slot. It integrates the filtering, diplexing, orthomode transducing, and radiating functions into a single element. Fou...

Liu, B, Ni, W, Liu, RP, Guo, YJ & Zhu, H 2023, 'Decentralized, Privacy-Preserving Routing of Cellular-Connected Unmanned Aerial Vehicles for Joint Goods Delivery and Sensing', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 9, pp. 9627-9641.
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Liu, B, Ni, W, Liu, RP, Guo, YJ & Zhu, H 2023, 'Optimal Routing of Unmanned Aerial Vehicle for Joint Goods Delivery and in-Situ Sensing', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 3, pp. 3594-3599.
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Liu, Y & Piccardi, M 2023, 'Topic-Based Unsupervised and Supervised Dictionary Induction', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 22, no. 3, pp. 1-21.
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Word translation is a natural language processing task that provides translation between the words of a source and a target language. As a task, it reduces to the induction of a bilingual dictionary, which is typically performed by aligning word embeddings of the source language to word embeddings of the target language. To date, all the existing approaches have focused on performing a single, global alignment in word embedding space. However, semantic differences between the various languages, in addition to differences in the content of the corpora used for training the word embeddings, can hinder the effectiveness of such a global alignment. For this reason, in this article we propose conducting the alignment between the source and target embedding spaces by multiple mappings at topic level. The experimental results show that our approach has been able to achieve an average accuracy improvement of +3.30 percentage points over a state-of-the-art approach in unsupervised dictionary induction from languages as diverse as German, French, Italian, Spanish, Finnish, Turkish, and Chinese to English, and +3.95 points average improvement in supervised dictionary induction.

Lyu, B, Zhou, C, Gong, S, Hoang, DT & Liang, Y-C 2023, 'Robust Secure Transmission for Active RIS Enabled Symbiotic Radio Multicast Communications', IEEE Transactions on Wireless Communications, vol. 22, no. 12, pp. 8766-8780.
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In this paper, we propose a robust secure transmission scheme for an active reconfigurable intelligent surface (RIS) enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). In the considered system, the active RIS is adopted to enable the secure transmission of primary signals from the primary transmitter to multiple primary users in a multicasting manner, and simultaneously achieve its own information delivery to the secondary user by riding over the primary signals. Taking into account the imperfect channel state information (CSI) related with Eves, we formulate the system power consumption minimization problem by optimizing the transmit beamforming and reflection beamforming for the bounded and statistical CSI error models, taking the worst-case SNR constraints and the SNR outage probability constraints at the Eves into considerations, respectively. Specifically, the S-Procedure and the Bernstein-Type Inequality are implemented to approximately transform the worst-case SNR and the SNR outage probability constraints into tractable forms, respectively. After that, the formulated problems can be solved by the proposed alternating optimization (AO) algorithm with the semi-definite relaxation and sequential rank-one constraint relaxation techniques. Numerical results show that the proposed active RIS scheme can reduce up to 27.0% system power consumption compared to the passive RIS.

Ma, C, Xu, Z, Hua, B, Zhang, Y, Shi, Q, Chu, L, Braun, R & Shi, J 2023, 'Random Body Movement Interference Mitigation in Radar Breath Detection Based on L1 Norm', IEEE Sensors Letters, vol. 7, no. 12, pp. 1-4.
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Ma, Y, Wu, N, Wu, K & Zhang, JA 2023, 'VAMP-Based Iterative Equalization for Index-Modulated Multicarrier FTN Signaling', IEEE Transactions on Communications, vol. 71, no. 4, pp. 2304-2316.
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Makhdoom, I, Abolhasan, M, Franklin, DR, Lipman, J, Zimmermann, C, Piccardi, M & Shariati, N 2023, 'Detecting compromised IoT devices: Existing techniques, challenges, and a way forward.', Comput. Secur., vol. 132, pp. 103384-103384.
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Ngo, QT, Jayawickrama, BA, He, Y & Dutkiewicz, E 2023, 'Multi-Agent DRL-Based RIS-Assisted Spectrum Sensing in Cognitive Satellite–Terrestrial Networks', IEEE Wireless Communications Letters, vol. 12, no. 12, pp. 2213-2217.
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This letter presents a novel approach for spectrum sensing in cognitive satellite-terrestrial networks. The approach uses multi-agent deep reinforcement learning (DRL) and reconfigurable intelligent surface to address the problem of under-utilization of terrestrial network spectrum in remote areas. Unlike previous studies that rely only on current sensing data, this approach utilizes historical data to improve spectrum detection accuracy and post-decision state to accelerate agent learning speed. Simulation results show that it outperforms existing DRL methods in terms of faster agent learning convergence and more effective detection of primary network spectrum occupancy.

Nguyen, CT, Hoang, DT, Nguyen, DN, Xiao, Y, Pham, H-A, Dutkiewicz, E & Tuong, NH 2023, 'FedChain: Secure Proof-of-Stake-Based Framework for Federated-Blockchain Systems', IEEE Transactions on Services Computing, vol. 16, no. 4, pp. 2642-2656.
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In this paper, we propose FedChain, a novel framework for federated-blockchain systems, to enable effective transferring of tokens between different blockchain networks. Particularly, we first introduce a federated-blockchain system together with a cross-chain transfer protocol to facilitate the secure and decentralized transfer of tokens between chains. We then develop a novel PoS-based consensus mechanism for FedChain, which can satisfy strict security requirements, prevent various blockchain-specific attacks, and achieve a more desirable performance compared to those of other existing consensus mechanisms. Moreover, a Stackelberg game model is developed to examine and address the problem of centralization in the FedChain system. Furthermore, the game model can enhance the security and performance of FedChain. By analyzing interactions between the stakeholders and chain operators, we can prove the uniqueness of the Stackelberg equilibrium and find the exact formula for this equilibrium. These results are especially important for the stakeholders to determine their best investment strategies and for the chain operators to design the optimal policy to maximize their benefits and security protection for FedChain. Simulations results then clearly show that the FedChain framework can help stakeholders to maximize their profits and the chain operators to design appropriate parameters to enhance FedChain’s security and performance.

Nguyen, CT, Nguyen, DN, Hoang, DT, Phan, KT, Niyato, D, Pham, H-A & Dutkiewicz, E 2023, 'Elastic Resource Allocation for Coded Distributed Computing Over Heterogeneous Wireless Edge Networks', IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2636-2649.
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Coded distributed computing (CDC) has recently emerged to be a promising solution to address the straggling effects in conventional distributed computing systems. By assigning redundant workloads to the computing nodes, CDC can significantly enhance the performance of the whole system. However, since the core idea of CDC is to introduce redundancies to compensate for uncertainties, it may lead to a large amount of wasted energy at the edge nodes. It can be observed that the more redundant workload added, the less impact the straggling effects have on the system. However, at the same time, the more energy is needed to perform redundant tasks. In this work, we develop a novel framework, namely CERA, to elastically allocate computing resources for CDC processes. Particularly, CERA consists of two stages. In the first stage, we model a joint coding and node selection optimization problem to minimize the expected processing time for a CDC task. Since the problem is NP-hard, we propose a linearization approach and a hybrid algorithm to quickly obtain the optimal solutions. In the second stage, we develop a smart online approach based on Lyapunov optimization to dynamically turn off straggling nodes based on their actual performance. As a result, wasteful energy consumption can be significantly reduced with minimal impact on the total processing time. Simulations using real-world datasets have shown that our proposed approach can reduce the system’s total processing time by more than 200% compared to that of the state-of-the-art approach, even when the nodes’ actual performance is not known in advance. Moreover, the results have shown that CERA’s online optimization stage can reduce the energy consumption by up to 37.14% without affecting the total processing time.

Nguyen, TV, Nguyen, DN, Renzo, MD & Zhang, R 2023, 'Leveraging Secondary Reflections and Mitigating Interference in Multi-IRS/RIS Aided Wireless Networks', IEEE Transactions on Wireless Communications, vol. 22, no. 1, pp. 502-517.
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Reconfigurable surfaces (RS) have recently emerged as an enabler for smart radio environments where they are used to actively tailor/control the radio propagation (e.g., to support users under adverse channel conditions). If multiple RSs are deployed (e.g., coated on various buildings) to support different groups of users, it is critical to jointly optimize the phase-shifts of all the RSs to mitigate interference amongst them as well as to leverage the secondary reflections amongst them. Motivated by these considerations, this paper considers the uplink transmissions of multiple users that are grouped and supported by multiple RSs to communicate with a multi-antenna base station (BS). We first formulate two optimization problems: the weighted sum-rate maximization and the minimum achievable rate (from all users) maximization. Unlike existing works that considered single user or single RS or multiple RSs without inter-RS reflections, the considered problems require the joint optimization of the phase-shifts of all RS elements and all beamformers at the multi-antenna BS. The two problems turn out to be non-convex and thus are difficult to be solved in general. Moreover, the inter-RS reflections give rise to the coupling of the phase-shifts amongst the RSs, making the optimization problems even more challenging to solve. To tackle them, we design alternating optimization algorithms that provably converge to locally optimal solutions. Simulation results reveal that by effectively mitigating interference and leveraging the secondary reflections amongst the RSs, there is a great benefit of deploying more RSs to support different groups of users so as to achieve a higher rate per user. This gain is even more significant with a larger number of elements per RS. Without properly dealing with the secondary reflections, by contrast, increasing the number of RSs can adversely impact the network throughput, especially for high transmit power.

Ni, Z, Zhang, JA & Liu, RP 2023, 'Waveform Optimizations Using Virtual Arrays in Broadband Radar Communications', IEEE Wireless Communications Letters, vol. 12, no. 5, pp. 912-916.
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Ni, Z, Zhang, JA, Wu, K & Liu, RP 2023, 'Uplink Sensing Using CSI Ratio in Perceptive Mobile Networks', IEEE Transactions on Signal Processing, vol. 71, pp. 2699-2712.
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Ni, Z, Zhang, JA, Wu, K, Yang, K & Liu, RP 2023, 'Receiver Design in Full-Duplex Joint Radar-Communication Systems', IEEE Transactions on Communications, vol. 71, no. 7, pp. 4234-4246.
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Nie, X, Chai, B, Wang, L, Liao, Q & Xu, M 2023, 'Learning enhanced features and inferring twice for fine-grained image classification', Multimedia Tools and Applications, vol. 82, no. 10, pp. 14799-14813.
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AbstractFine-Grained Visual Categorization (FGVC) aims to distinguish between extremely similar subordinate-level categories within the same basic-level category. Existing research has proven the great importance of the discriminative features in FGVC but ignored the contributions for correct classification from other features, and the extracted features always contain more information about the obvious regions but less about subtle regions. In this paper, firstly, a novel module named forcing module is proposed to force the network to extract more diverse features for FGVC, which generates a suppression mask based on the class activation maps to suppress the most distinguishable regions, so as to force the network to extract other secondary distinguishable features as the final features. The forcing module consists of the original branch and the forcing branch. The original branch focuses on the primary discriminative regions while the forcing branch focuses on secondary discriminative regions. Secondly, in order to solve the problem that information of small-scale distinguishable features is lost seriously after multi-layer down-sampling, according to the class activation maps of the first prediction, the object is cropped and scaled as the second input. To reduce the prediction error, the first and second prediction probabilities are fused as the final prediction result. Experimental results indicate that the proposed method not only outperforms the baseline model by a large margin (3.7%, 5.9%, 3.1% respectively) on CUB-200-2011, Stanford-Cars, and FGVC-Aircraft, but also achieves state-of-the-art performance on FGVC-Aircraft.

Qi, T, Lyu, B & Hoang, DT 2023, 'Pilot Sequences With Low Coherence and PAPR for Grant-Free Massive Access', IEEE Wireless Communications Letters, vol. 12, no. 7, pp. 1254-1258.
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To accommodate massive devices and facilitate activity detection in a grant-free access system, it is necessary to design non-orthogonal pilot sequences with low coherence. In this letter, we study the optimization problem to minimize the worst-case coherence among sequences under the peak-to-average power ratio (PAPR) constraint of each sequence. An efficient method is proposed to iteratively construct sequences by the conjugate gradient descent and space projection. Simulation results demonstrate that the proposed sequences can decrease the coherence under strict PAPR constraints by up to 62% compared with the benchmarks.

Raza, A, Keshavarz, R, Dutkiewicz, E & Shariati, N 2023, 'Compact Multiservice Antenna for Sensing and Communication Using Reconfigurable Complementary Spiral Resonator', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-9.
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Raza, MA, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2023, 'Statistical Learning-Based Adaptive Network Access for the Industrial Internet of Things', IEEE Internet of Things Journal, vol. 10, no. 14, pp. 12219-12233.
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Reinhartz-Berger, I, Zdravkovic, J & Gill, A 2023, 'Guest editorial for EMMSAD’2021 special section', Software and Systems Modeling, vol. 22, no. 1, pp. 9-11.
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Sang, L, Xu, M, Qian, S & Wu, X 2023, 'Adversarial Heterogeneous Graph Neural Network for Robust Recommendation', IEEE Transactions on Computational Social Systems, vol. 10, no. 5, pp. 2660-2671.
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Saputra, YM, Hoang, DT, Nguyen, DN, Tran, L-N, Gong, S & Dutkiewicz, E 2023, 'Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles', IEEE Transactions on Mobile Computing, vol. 22, no. 4, pp. 2100-2115.
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Federated learning (FL) can empower Internet-of-Vehicles (IoV) to help the vehicular service provider (VSP) improve the global model accuracy for road safety and better profits for both VSP and participating smart vehicles (SVs). Nonetheless, there exist major challenges when implementing FL in IoV including dynamic activities and diverse quality-of-information (QoI) from a large number of SVs, VSP's limited payment budget, and profit competition among SVs. In this paper, we propose a novel dynamic FL-based economic framework for an IoV network to address these challenges. Specifically, the VSP first implements an SV selection method to determine a set of the best SVs for the FL process according to the significance of their current locations and information at each learning round. Then, each selected SV can collect on-road information and offer a payment contract to the VSP based on its collected QoI. For that, we develop a multi-principal one-agent contract-based policy to maximize the profits of the VSP and learning SVs under the asymmetric information between them. Through experimental results using real-world on-road datasets, we show that our framework can converge 57% faster and obtain social welfare of the network up to 27.2 times compared with those of other baseline FL methods.

Seifollahi, S & Piccardi, M 2023, 'Taxonomy-Based Feature Extraction for Document Classification, Clustering and Semantic Analysis', pp. 575-586.
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Shan, B, Ni, W, Yuan, X, Yang, D, Wang, X & Liu, RP 2023, 'Graph learning from band-limited data by graph Fourier transform analysis', Signal Processing, vol. 207, pp. 108950-108950.
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Shan, B, Yuan, X, Ni, W, Wang, X, Liu, RP & Dutkiewicz, E 2023, 'Novel Graph Topology Learning for Spatio-Temporal Analysis of COVID-19 Spread', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 6, pp. 2693-2704.
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Shan, B, Yuan, X, Ni, W, Wang, X, Liu, RP & Dutkiewicz, E 2023, 'Preserving the Privacy of Latent Information for Graph-Structured Data', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 5041-5055.
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Song, L-Z, Ansari, M, Qin, P-Y, Maci, S, Du, J & Guo, YJ 2023, 'Two-Dimensional Wide-Angle Multibeam Flat GRIN Lens With a High Aperture Efficiency', IEEE Transactions on Antennas and Propagation, vol. 71, no. 10, pp. 8018-8029.
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High-aperture-efficiency 2-dimensional (2-D) multi-beam flat gradient-index (GRIN) lenses are developed in this work. New methods based on bifocal analysis are found to determine the refractive-index profile of the lens as well as feed positions along a circular feed locus to enable independent wide-angle multi-beam radiations. Distinct formulas for the GRIN profile are provided for two different purposes: i) multi-beam with good average performance in all azimuthal planes or ii) radiation performance optimized in a specific azimuthal plane. The latter solution requires a GRIN variation in both azimuthal and radial variables. Subwavelength triple-metal-layer unit cells are designed to emulate the local refractive indices. A 2-D multi-beam GRIN lens, fed by 13-modules of 2×2 patch arrays displaced along xoz and yoz focus loci, has been successfully simulated, fabricated, and measured. Wide-angle multi-beam radiations have been obtained with a beam coverage of around ±45° in both xoz and yoz planes. The multi-beam radiation patterns are stable in a 22.2% bandwidth from 12 GHz to 15 GHz. The beam-scanning losses in this operating band are 1-2.6 dB and 2.1-3.9 dB in xoz and yoz planes, respectively. The measured peak realized gain is 22.3 dBi at 13.4 GHz, corresponding to an aperture efficiency of 66.4%.

Song, X, Lu, X, Fang, G, He, X, Fan, X, Cai, L, Jia, W & Wang, Z 2023, 'ABUSDet: A Novel 2.5D deep learning model for automated breast ultrasound tumor detection', Applied Intelligence, vol. 53, no. 21, pp. 26255-26269.
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Automated Breast Ultrasound is a highly advanced breast tumor detection modality that produces hundreds of 2D slices in each scan. However, this large number of slices poses a significant burden for physicians to review. This paper proposes a novel 2.5D tumor detection model, named “ABUSDet,” to assist physicians in automatically reviewing ABUS images and predicting the locations of breast tumors in images. At the core of this approach, a sequence of data blocks partitioned from a pre-processed 3D volume are fed to a 2.5D tumor detection model, which outputs a sequence of 2D tumor candidates. An aggregation module then clusters the 2D tumor candidates to produce the ultimate 3D coordinates of the tumors. To further improve the accuracy of the model, a novel mechanism for training deep learning models, called “Deliberate Training,” is proposed. The proposed model is trained and tested on a dataset of 87 patients with 235 ABUS volumes. It achieves sensitivities of 77.94%, 75.49%, and 65.19% at FPs/volume of 3, 2, and 1, respectively. Compared with the 2D and 3D object detection models, the proposed ABUSDet model achieves the highest sensitivity with relatively low false-positive rates. Graphical abstract: [Figure not available: see fulltext.]

Sun, S-Y, Ding, C, Jiang, W & Guo, YJ 2023, 'Simultaneous Suppression of Cross-Band Scattering and Coupling Between Closely Spaced Dual-Band Dual-Polarized Antennas', IEEE Transactions on Antennas and Propagation, vol. 71, no. 8, pp. 6423-6434.
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Sun, Y, Zhang, JA, Wu, K & Liu, RP 2023, 'Frequency-Domain Sensing in Time-Varying Channels', IEEE Wireless Communications Letters, vol. 12, no. 1, pp. 16-20.
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Joint Communications and Sensing (JCAS) in mobile networks are typically based on Orthogonal Frequency Division Multiplexing (OFDM) systems. For time-varying channels, large Doppler frequencies in OFDM JCAS can cause notable intercarrier interference, which has not been considered for sensing. In this paper, we propose a frequency-domain sensing framework for OFDM JCAS systems. We first derive a frequency-domain closed-form expression of the received signals, to characterise the delay and Doppler frequency impact within and across OFDM blocks. We then develop intra-block and inter-block sensing algorithms, based on the expression. The framework is further completed with exemplified pilot design and periodogram sensing algorithm. Simulation results demonstrate the effectiveness of the proposed framework.

Tong, M, Huang, X & Zhang, JA 2023, 'Faster-Than-Nyquist Transmission With Frame-by-Frame Decision-Directed Successive Interference Cancellation', IEEE Transactions on Communications, vol. 71, no. 8, pp. 4851-4861.
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Tran, LC, Le, AT, Huang, X, Dutkiewicz, E, Ngo, D & Taparugssanagorn, A 2023, 'Complexity Reduction for Hybrid TOA/AOA Localization in UAV-Assisted WSNs', IEEE Sensors Letters, vol. 7, no. 11, pp. 1-4.
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Tuan, HD, Nasir, AA, Chen, Y, Dutkiewicz, E & Poor, HV 2023, 'Quantized RIS-Aided Multi-User Secure Beamforming Against Multiple Eavesdroppers', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 4695-4706.
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Ullah, MA, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 2023, 'Multiservice Compact Pixelated Stacked Antenna With Different Pixel Shapes for IoT Applications', IEEE Internet of Things Journal, vol. 10, no. 22, pp. 19883-19897.
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Unanue, IJ, Borzeshi, EZ & Piccardi, M 2023, 'Regressing Word and Sentence Embeddings for Low-Resource Neural Machine Translation', IEEE Transactions on Artificial Intelligence, vol. 4, no. 3, pp. 450-463.
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Unanue, IJ, Haffari, G & Piccardi, M 2023, 'T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification', Transactions of the Association for Computational Linguistics, vol. 11, pp. 1147-1161.
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Abstract Cross-lingual text classification leverages text classifiers trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning (zero/ few-shots cross-lingual transfer). Nowadays, cross-lingual text classifiers are typically built on large-scale, multilingual language models (LMs) pretrained on a variety of languages of interest. However, the performance of these models varies significantly across languages and classification tasks, suggesting that the superposition of the language modelling and classification tasks is not always effective. For this reason, in this paper we propose revisiting the classic “translate-and-test” pipeline to neatly separate the translation and classification stages. The proposed approach couples 1) a neural machine translator translating from the targeted language to a high-resource language, with 2) a text classifier trained in the high-resource language, but the neural machine translator generates “soft” translations to permit end-to-end backpropagation during fine-tuning of the pipeline. Extensive experiments have been carried out over three cross-lingual text classification datasets (XNLI, MLDoc, and MultiEURLEX), with the results showing that the proposed approach has significantly improved performance over a competitive baseline.

Vu, L, Nguyen, QU, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2023, 'Deep Generative Learning Models for Cloud Intrusion Detection Systems', IEEE Transactions on Cybernetics, vol. 53, no. 1, pp. 565-577.
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Intrusion detection (ID) on the cloud environment has received paramount interest over the last few years. Among the latest approaches, machine learning-based ID methods allow us to discover unknown attacks. However, due to the lack of malicious samples and the rapid evolution of diverse attacks, constructing a cloud ID system (IDS) that is robust to a wide range of unknown attacks remains challenging. In this article, we propose a novel solution to enable robust cloud IDSs using deep neural networks. Specifically, we develop two deep generative models to synthesize malicious samples on the cloud systems. The first model, conditional denoising adversarial autoencoder (CDAAE), is used to generate specific types of malicious samples. The second model (CDAEE-KNN) is a hybrid of CDAAE and the K-nearest neighbor algorithm to generate malicious borderline samples that further improve the accuracy of a cloud IDS. The synthesized samples are merged with the original samples to form the augmented datasets. Three machine learning algorithms are trained on the augmented datasets and their effectiveness is analyzed. The experiments conducted on four popular IDS datasets show that our proposed techniques significantly improve the accuracy of the cloud IDSs compared with the baseline technique and the state-of-the-art approaches. Moreover, our models also enhance the accuracy of machine learning algorithms in detecting some currently challenging distributed denial of service (DDoS) attacks, including low-rate DDoS attacks and application layer DDoS attacks.

Wang, X, Qin, P-Y, Song, L-Z, Jin, R & Guo, YJ 2023, 'Tightly Coupled Huygens Element-Based Conformal Transmitarray for E-Band Airborne Communication Systems', IEEE Transactions on Antennas and Propagation, vol. 71, no. 3, pp. 2467-2475.
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In this article, a wideband conformal transmitarray employing dual-layer tightly coupled Huygens elements is proposed at E-band. The element consists of five pairs of partly overlapped metallic strips with different lengths printed on two sides of a dielectric substrate. It can support tightly coupled Huygens resonances with a high transmission efficiency and a nearly full phase coverage in a wide bandwidth from 71 to 87 GHz. Equivalent circuit models are created to analyze the tightly coupled Huygens element, which has good agreement with that from full-wave simulations. In order to validate the proposed element, a cylindrically conformal transmitarray at 78 GHz is designed, fabricated, and measured. Good agreement between the measured and simulated results has been obtained, showing a peak realized gain of 26.6 dBi with an aperture efficiency of 37.2% from measurement. A measured 3 dB gain bandwidth of 20.4% is achieved from 71 to 87 GHz, fully covering the E-band spectrum.

Wang, Z, Zhang, JA, Xu, M & Guo, YJ 2023, 'Single-Target Real-Time Passive WiFi Tracking', IEEE Transactions on Mobile Computing, vol. 22, no. 6, pp. 3724-3742.
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Wei, F, Liu, X, Ding, X-Z, Zhao, X-B & Qin, P-Y 2023, 'A Balanced Filtering Antenna Array With High Gain, Steep Selectivity, and Multiradiation Nulls Parallel-Fed by Differential Broadband Network', IEEE Transactions on Antennas and Propagation, vol. 71, no. 12, pp. 9926-9931.
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In this communication, a 2 × 2 balanced filtering antenna array parallel-fed by a differential broadband network is presented. The array has four identical stacked filtering antenna elements, each of which consists of a main patch etched with a folded U-shaped slot, a pair of ear-shaped parasitic patches, a stacked patch, and a folded T-shaped strip. The conceived antenna topology can enhance the gain flatness, improve selectivity, and increase the number of radiation nulls. Meanwhile, by introducing a novel ring-shaped transition structure (RSTS) to the balanced-to-single-ended (BTSE) four-way feeding network, a broad bandwidth, low insertion losses, low phase difference errors, and high common-mode (CM) suppression are realized. To validate the method, an integrated antenna array with a center frequency at 2.5 GHz is fabricated and measured. Experimental results exhibit a boresight gain of 13.7 dBi, four radiation nulls, and a square factor (SF₁₀) of 1.21.

Wen, Y, Qin, P-Y, Maci, S & Guo, YJ 2023, 'Low-Profile Multibeam Antenna Based on Modulated Metasurface', IEEE Transactions on Antennas and Propagation, vol. 71, no. 8, pp. 6568-6578.
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Linearly polarized (LP) multiport multibeam antennas (MBAs) based on modulated metasurface (MTS) are presented in this article. A key challenge for MTS to generate multiple independent beams is the mutual interference of different impedance modulations. This interference can lead to reduced directivities of main beams and high sidelobe levels (SLLs). A full-wave-based optimization for a large number of beams is significantly time-consuming and practically ineffective. The source locations are optimized here by employing the aperture field calculated from a zeroth-order approximation (ZOA) of the currents on the surface. This provides a good preliminary accuracy without any full-wave analysis. The relevant design method is verified by two examples of three-beam and seven-beam MTS antennas. Furthermore, the seven-beam MTS antenna is fabricated and successfully measured. The measurements show a measured realized gain of 20 dBi at 14.10 GHz with a beam coverage (10-dB overlap) up to ±36°. It is seen that the same performance requires larger areas with conventional summation of apertures.

Wu, K & Guo, YJ 2023, 'Deterministic Solutions to Improved Generalized Joined Coupler Matrix for Multibeam Antennas', IEEE Transactions on Antennas and Propagation, vol. 71, no. 12, pp. 9454-9466.
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Wu, K, Zhang, JA, Huang, X & Guo, YJ 2023, 'OTFS-Based Joint Communication and Sensing for Future Industrial IoT', IEEE Internet of Things Journal, vol. 10, no. 3, pp. 1973-1989.
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Effective wireless communications are increasingly important in maintaining the successful closed-loop operation of mission-critical industrial Internet-of-Things (IIoT) applications. To meet the ever-increasing demands on better wireless communications for IIoT, we propose an orthogonal time-frequency space (OTFS) waveform-based joint communication and radio sensing (JCAS) scheme -an energy-efficient solution for not only reliable communications but also high-accuracy sensing. OTFS has been demonstrated to have higher reliability and energy efficiency than the currently popular IIoT communication waveforms. JCAS has also been highly recommended for IIoT, since it saves cost, power and spectrum compared to having two separate radio frequency systems. Performing JCAS based on OTFS, however, can be hindered by a lack of effective OTFS sensing. This paper is dedicated to filling this technology gap. We first design a series of echo pre-processing methods that successfully remove the impact of communication data symbols in the time-frequency domain, where major challenges, like inter-carrier and inter-symbol interference and noise amplification, are addressed. Then, we provide a comprehensive analysis of the signal-to-interference-plus-noise ratio (SINR) for sensing and optimize a key parameter of the proposed method to maximize the SINR. Extensive simulations show that the proposed sensing method approaches the maximum likelihood estimator with respect to the estimation accuracy of target parameters and manifests applicability to wide ranges of key system parameters. Notably, the complexity of the proposed method is only dominated by a two-dimensional Fourier transform.

Wu, K, Zhang, JA, Huang, X, Guo, YJ & Hanzo, L 2023, 'Simultaneous Beam and User Selection for the Beamspace mmWave/THz Massive MIMO Downlink', IEEE Transactions on Communications, vol. 71, no. 3, pp. 1785-1797.
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Wu, K, Zhang, JA, Huang, X, Heath, RW & Guo, YJ 2023, 'Green Joint Communications and Sensing Employing Analog Multi-Beam Antenna Arrays', IEEE Communications Magazine, vol. 61, no. 7, pp. 172-178.
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Joint communications and sensing (JCAS) is potentially a hallmark technology for the sixth generation mobile network (6G). Most existing JCAS designs are based on digital arrays, analog arrays with tunable phase shifters, or hybrid arrays, which are effective but are generally complicated to design and power inefficient. This article introduces the energyefficient and easy-to-design multi-beam antenna arrays (MBAAs) for JCAS. Using pre-designed and fixed analog devices, such as lens or Butler matrix, MBAA can simultaneously steer multiple beams yet with negligible power consumption compared with other techniques. Moreover, MBAAs enable flexible beam synthesis, accurate angle-of-arrival estimation, and easy handling/ utilization of the beam squint effect. All these features have not been well captured by the JACS community yet. To promote the awareness of them, we intuitively illustrate them and also exploit them for constructing a multi-beam JCAS framework. Finally, the challenges and opportunities are discussed to foster the development of green JCAS systems.

Xia, J, Xu, M, Zhang, H, Zhang, J, Huang, W, Cao, H & Wen, S 2023, 'Robust Face Alignment via Inherent Relation Learning and Uncertainty Estimation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 8, pp. 10358-10375.
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Xiao, D, Zhang, JA, Liu, X, Qu, Y, Ni, W & Liu, RP 2023, 'A Two-Stage GCN-Based Deep Reinforcement Learning Framework for SFC Embedding in Multi-Datacenter Networks', IEEE Transactions on Network and Service Management, vol. 20, no. 4, pp. 4297-4312.
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Xiao, Y, Xia, R, Li, Y, Shi, G, Nguyen, DN, Hoang, DT, Niyato, D & Krunz, M 2023, 'Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach', IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp. 1-15.
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With the rising demand for wireless services and increased awareness of the need for data protection, existing network traffic analysis and management architectures are facing unprecedented challenges in classifying and synthesizing the increasingly diverse services and applications. This paper proposes FS-GAN, a federated self-supervised learning framework to support automatic traffic analysis and synthesis over a large number of heterogeneous datasets. FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset. A federated learning-based framework is adopted to coordinate local model training processes of different GANs across different datasets. FS-GAN can classify data of unknown types of service and create synthetic samples that capture the traffic distribution of the unknown types. We prove that FS-GAN can minimize the Jensen-Shannon Divergence (JSD) between the distribution of real data across all the datasets and that of the synthesized data samples. FS-GAN also maximizes the JSD among the distributions of data samples created by different generators, resulting in each generator producing synthetic data samples that follow the same distribution as one particular service type. Extensive simulation results show that the classification accuracy of FS-GAN achieves over $20\%$ improvement in average compared to the state-of-the-art clustering-based traffic analysis algorithms. FS-GAN also has the capability to synthesize highly complex mixtures of traffic types without requiring any human-labeled data samples.

Xiao, Y, Zhang, X, Li, Y, Shi, G, Krunz, M, Nguyen, DN & Hoang, DT 2023, 'Time-sensitive Learning for Heterogeneous Federated Edge Intelligence', IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp. 1-18.
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Real-time machine learning (ML) has recently attracted significant interest due to its potential to support instantaneous learning, adaptation, and decision making in a wide range of application domains, including self-driving vehicles, intelligent transportation, and industry automation. In this paper, we investigate real-time ML in a federated edge intelligence (FEI) system, an edge computing system that implements federated learning (FL) solutions based on data samples collected and uploaded from decentralized data networks, e.g., Internet-of-Things (IoT) and/or wireless sensor networks. FEI systems often exhibit heterogenous communication and computational resource distribution, as well as non-i.i.d. data samples arrived at different edge servers, resulting in long model training time and inefficient resource utilization. Motivated by this fact, we propose a time-sensitive federated learning (TS-FL) framework to minimize the overall run-time for collaboratively training a shared ML model with desirable accuracy. Training acceleration solutions for both TS-FL with synchronous coordination (TS-FL-SC) and asynchronous coordination (TS-FL-ASC) are investigated. To address the straggler effect in TS-FL-SC, we develop an analytical solution to characterize the impact of selecting different subsets of edge servers on the overall model training time. A server dropping-based solution is proposed to allow some slow-performance edge servers to be removed from participating in the model training if their impact on the resulting model accuracy is limited. A joint optimization algorithm is proposed to minimize the overall time consumption of model training by selecting participating edge servers, the local epoch number (the number of model training iterations per coordination), and the data batch size (the number of data samples for each model training iteration). Motivated by the fact that data samples at the slowest edge server may exhibit special characteristi...

Xu, C, Jia, W, Cui, T, Wang, R, Zhang, Y-F & He, X 2023, 'Arbitrary-Shape Scene Text Detection via Visual-Relational Rectification and Contour Approximation', IEEE Transactions on Multimedia, vol. 25, no. 99, pp. 4052-4066.
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One trend in the latest bottom-up approaches for arbitrary-shape scene text detection is to determine the links between text segments using Graph Convolutional Networks (GCNs). However, the performance of these bottom-up methods is still inferior to that of state-of-the-art top-down methods even with the help of GCNs. We argue that a cause of this is that bottom-up methods fail to make proper use of visual-relational features, which results in accumulated false detection, as well as the error-prone route-finding used for grouping text segments. In this paper, we improve classic bottom-up text detection frameworks by fusing the visual-relational features of text with two effective false positive/negative suppression (FPNS) mechanisms and developing a new shape-approximation strategy. First, dense overlapping text segments depicting the ‘`characterness’' and ‘`streamline’' properties of text are constructed and used in weakly supervised node classification to filter the falsely detected text segments. Then, relational features and visual features of text segments are fused with a novel Location-Aware Transfer (LAT) module and Fuse Decoding (FD) module to jointly rectify the detected text segments. Finally, a novel multiple-text-map-aware contour-approximation strategy is developed based on the rectified text segments, instead of the error-prone route-finding process, to generate the final contour of the detected text. Experiments conducted on five benchmark datasets demonstrate that our method outperforms the state-of-the-art performance when embedded in a classic text detection framework, which revitalizes the strengths of bottom-up methods.

Xu, C, Jia, W, Wang, R, He, X, Zhao, B & Zhang, Y 2023, 'Semantic Navigation of PowerPoint-Based Lecture Video for AutoNote Generation', IEEE Transactions on Learning Technologies, vol. 16, no. 1, pp. 1-17.
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Xu, C, Jia, W, Wang, R, Luo, X & He, X 2023, 'MorphText: Deep Morphology Regularized Accurate Arbitrary-Shape Scene Text Detection', IEEE Transactions on Multimedia, vol. 25, no. 99, pp. 4199-4212.
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Bottom-up text detection methods play an important role in arbitrary-shape scene text detection but there are two restrictions preventing them from achieving their great potential, i.e., 1) the accumulation of false text segment detections, which affects subsequent processing, and 2) the difficulty of building reliable connections between text segments. Targeting these two problems, we propose a novel approach, named ``MorphText', to capture the regularity of texts by embedding deep morphology for arbitrary-shape text detection. Towards this end, two deep morphological modules are designed to regularize text segments and determine the linkage between them. First, a Deep Morphological Opening (DMOP) module is constructed to remove false text segment detections generated in the feature extraction process. Then, a Deep Morphological Closing (DMCL) module is proposed to allow text instances of various shapes to stretch their morphology along their most significant orientation while deriving their connections.Extensive experiments conducted on four challenging benchmark datasets (CTW1500, Total-Text, MSRA-TD500 and ICDAR2017) demonstrate that our proposed MorphText outperforms both top-down and bottom-up state-of-the-art arbitrary-shape scene text detection approaches.

Yang, L, Zhu, X & Gómez-García, R 2023, 'High-Order Quasi-Elliptic-Type Wideband Bandpass Filter With Ultrabroad Input-Reflectionless Stopband Range', IEEE Microwave and Wireless Technology Letters, vol. 33, no. 6, pp. 655-658.
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Yang, S, Wu, S, Yang, E, Han, B, Liu, Y, Xu, M, Niu, G & Liu, T 2023, 'A Parametrical Model for Instance-Dependent Label Noise', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 12, pp. 14055-14068.
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Yao, L, Kusakunniran, W, Zhang, P, Wu, Q & Zhang, J 2023, 'Improving Disentangled Representation Learning for Gait Recognition Using Group Supervision', IEEE Transactions on Multimedia, vol. 25, no. 99, pp. 4187-4198.
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In decades, gait has been gathering extensive interest for the advantage that it can be measured from a distance without physical contact. However, for image/video-based gait recognition, its performance can be remarkably influenced by exterior factors, such as viewing angles and clothing changes. Thus, in this paper, a group-supervised disentangled representation learning network is proposed for gait recognition to extract features invariant to these factors. First, sequences are explicitly disentangled into pose, gait, appearance, and view features through a generic encoder-decoder framework. To ensure the feature adaptability and independency, a disentanglement swap module is specifically adopted during our encode-decoder process through a series of swap operations based on the feature attributes. Following the feature disentanglement, a disentanglement aggregation module is also specially proposed for pose, gait, and appearance features to enhance their effectiveness. Finally, the enhanced three features are concatenated together for gait recognition. Relevant experiments certify that compared with other disentangled representation learning-based gait recognition methods, our proposed method enables to obtain a more excellent recognition result, despite fewer gait frames being utilized.

Yu, G, Wang, X, Ni, W, Lu, Q, Xu, X, Liu, RP & Zhu, L 2023, 'Adaptive Resource Scheduling in Permissionless Sharded-Blockchains: A Decentralized Multiagent Deep Reinforcement Learning Approach', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 11, pp. 7256-7268.
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Yu, G, Wang, X, Sun, C, Yu, P, Ni, W & Liu, RP 2023, 'Obfuscating the Dataset: Impacts and Applications', ACM Transactions on Intelligent Systems and Technology, vol. 14, no. 5, pp. 1-15.
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Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties when dataset sharing is essential. We conduct comprehensive experiments to investigate how the dataset obfuscation can affect the resultant model weights —in terms of the model accuracy, ℓ 2 -distance-based model distance, and level of data privacy—and discuss the potential applications with the proposed Privacy, Utility, and Distinguishability (PUD)-triangle diagram to visualize the requirement preferences. Our experiments are based on the popular MNIST and CIFAR-10 datasets under both independent and identically distributed (IID) and non-IID settings. Significant results include a tradeoff between the model accuracy and privacy level and a tradeoff between the model difference and privacy level. The results indicate broad application prospects for training outsourcing and guarding against attacks in federated learning both of which have been increasingly attractive in many areas, particularly learning in edge computing.

Yu, H, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2023, 'Low-Resolution Hybrid Beamforming in Millimeter-Wave Multi-User Systems', IEEE Transactions on Vehicular Technology, vol. 72, no. 7, pp. 8941-8955.
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Yu, H, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2023, 'Regularized Zero-Forcing Aided Hybrid Beamforming for Millimeter-Wave Multiuser MIMO Systems', IEEE Transactions on Wireless Communications, vol. 22, no. 5, pp. 3280-3295.
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This paper considers hybrid beamforming consisting of analog beamforming (ABF) coupled with digital baseband beamforming (DBF) which is designed for multi-user (MU) multiple input multiple output (MIMO) millimeter-wave (mmWave) communications. ABF uses a limited number of radio frequency (RF) chains and finite-resolution phase-shifters to alleviate the power consumption at the base station (BS), while DBF uses either zero-forcing beamforming (ZFB) or regularized zero forcing beamforming (RZFB) to restrain MU interference. The joint design of ABF and DBF constitutes a computationally challenging mixed discrete continuous optimization problem. The paper develops efficient algorithms for its solution, which iterate scalable-complex expressions. Furthermore, we conceive a new class of MU RZFB for attaining higher rates. Simulations are provided to demonstrate the viability of the proposed algorithms and the advantages of the conceived RZFB.

Yuan, X, Hu, S, Ni, W, Liu, RP & Wang, X 2023, 'Joint User, Channel, Modulation-Coding Selection, and RIS Configuration for Jamming Resistance in Multiuser OFDMA Systems', IEEE Transactions on Communications, vol. 71, no. 3, pp. 1631-1645.
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Zeng, FC, Ding, C, Chen, Y-N & Guo, YJ 2023, 'A Wide-Beam Antenna Array With Spatially Variable-Orthogonal-Polarizations (SVOP) Achieved by Polarization-Mixing', IEEE Transactions on Antennas and Propagation, vol. 71, no. 9, pp. 7626-7631.
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Zeng, J, Wu, T, Feng, W, Ni, W, Lv, T, Zhou, S, Wang, X & Guo, YJ 2023, 'Analysis of Massive Ultra-Reliable and Low-Latency Communications Over the κ-μ Shadowed Fading Channel', IEEE Transactions on Communications, vol. 71, no. 3, pp. 1798-1813.
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Zeng, J, Xiao, C, Wu, T, Ni, W, Liu, RP & Guo, YJ 2023, 'Uplink Non-Orthogonal Multiple Access With Statistical Delay Requirement: Effective Capacity, Power Allocation, and α Fairness', IEEE Transactions on Wireless Communications, vol. 22, no. 2, pp. 1298-1313.
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Zhang, C, Xu, RYD, Zhang, X & Huang, W 2023, 'Capture and control content discrepancies via normalised flow transfer', Pattern Recognition Letters, vol. 165, pp. 161-167.
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Zhang, G, Feng, S, Chen, Y, Yang, Y, Zhu, H, Zhou, X, Hong, J, Tang, W & Yang, J 2023, '3-D Printed Filtering Rat-Race Couplers Using Hemispherical Cavity Resonator', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 11, pp. 4922-4932.
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Zhang, H & Xu, M 2023, 'Multiscale Emotion Representation Learning for Affective Image Recognition', IEEE Transactions on Multimedia, vol. 25, pp. 2203-2212.
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Zhang, H & Xu, M 2023, 'Recognition of Emotions in User-Generated Videos through Frame-Level Adaptation and Emotion Intensity Learning', IEEE Transactions on Multimedia, vol. 25, pp. 881-891.
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Zhang, JA, Wu, K, Huang, X & Guo, YJ 2023, 'Beam Alignment for Analog Arrays Based on Gaussian Approximation', IEEE Transactions on Vehicular Technology, vol. 72, no. 6, pp. 8152-8157.
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Beam alignment is a process for receive and transmit antenna arrays to find the correct beamforming directions. It is typically based on beam scanning and peak-energy searching, which lead to time-consuming beam training process in communication protocols, such as 802.11ad for vehicular networks. In this correspondence, we propose a fast beam alignment method for analog arrays, based on directly estimating the angle-of-arrival (AoA) of the incoming signals. We propose simple and highly efficient AoA estimators, by approximating the power of the array response as a Gaussian function. One estimator is based on the power ratio and can coherently combine multiple measurements scanned at arbitrary intervals and with different beam widths. The other two are based on an innovative idea of Gaussian curve fitting with weighted least square techniques, and one of them can even work without knowing the beam width. Simulation results validate the effectiveness of the proposed scheme.

Zhang, X, Xia, W, Cui, Q, Tao, X & Liu, RP 2023, 'Efficient and Trusted Data Sharing in a Sharding-Enabled Vehicular Blockchain', IEEE Network, vol. 37, no. 2, pp. 230-237.
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Zhou, C, Lyu, B, Feng, Y & Hoang, DT 2023, 'Transmit Power Minimization for STAR-RIS Empowered Symbiotic Radio Communications', IEEE Transactions on Cognitive Communications and Networking, vol. 9, no. 6, pp. 1641-1656.
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In this paper, we propose a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) empowered transmission scheme for symbiotic radio (SR) systems to make more flexibility for network deployment and enhance system performance. The STAR-RIS is utilized to not only beam the primary signals from the base station (BS) towards multiple primary users on the same side of the STAR-RIS, but also achieve the secondary transmission to the secondary users on another side. We consider both the broadcasting signal model and unicasting signal model at the BS. For each model, we aim for minimizing the transmit power of the BS by designing the active beamforming and simultaneous reflection and transmission coefficients under the practical phase correlation constraint. To address the challenge of solving the formulated problem, we propose a block coordinate descent based algorithm with the semidefinite relaxation, penalty dual decomposition and successive convex approximation methods, which decomposes the original problem into one sub-problem about active beamforming and the other sub-problem about simultaneous reflection and transmission coefficients, and iteratively solve them until the convergence is achieved. Numerical results indicate that the proposed scheme can reduce up to 150.6% transmit power compared to the backscattering device enabled scheme.

Zhou, H, Long, Y, Gong, S, Zhu, K, Hoang, DT & Niyato, D 2023, 'Hierarchical Multi-Agent Deep Reinforcement Learning for Energy-Efficient Hybrid Computation Offloading', IEEE Transactions on Vehicular Technology, vol. 72, no. 1, pp. 986-1001.
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Mobile edge computing (MEC) provides an economical way for the resource-constrained edge users to offload computational workload to MEC servers co-located with the access point (AP). In this paper, we consider a hybrid computation offloading scheme that allows edge users to offload workloads by using active RF communications and backscatter communications. We aim to maximize the overall energy efficiency subject to the completion of all workload by jointly optimizing the AP's beamforming and the users' offloading decisions. Considering a dynamic environment, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework to solve this problem. The high-level agent resides in the AP and optimizes the beamforming strategy, while the low-level user agents learn and adapt individuals' offloading strategies. To further improve the learning efficiency, we propose a novel optimization-driven learning algorithm that allows the AP to estimate the low-level users' actions by solving approximate optimization problem efficiently. Then, the action estimation can be shared with all users and drive them to update individuals' actions independently. Simulation results reveal that our algorithm can improve the system performance by 50%. The learning efficiency and reliability are also improved significantly comparing to the model-free learning methods.

Zhou, I, Lipman, J, Abolhasan, M & Shariati, N 2023, 'Intelligent spatial interpolation-based frost prediction methodology using artificial neural networks with limited local data', Environmental Modelling & Software, vol. 165, pp. 105724-105724.
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Zhu, W, Tuan, HD, Dutkiewicz, E, Fang, Y & Hanzo, L 2023, 'Low-Complexity Pareto-Optimal 3D Beamforming for the Full-Dimensional Multi-User Massive MIMO Downlink', IEEE Transactions on Vehicular Technology, vol. 72, no. 7, pp. 8869-8885.
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Zhu, W, Tuan, HD, Dutkiewicz, E, Fang, Y, Poor, HV & Hanzo, L 2023, 'Long-Term Rate-Fairness-Aware Beamforming Based Massive MIMO Systems', IEEE Transactions on Communications, pp. 1-1.
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Alsufyani, N & Gill, AQ 1970, 'A Knowledge-Graph Based Integrated Digital EA Maturity and Performance Framework', Lecture Notes in Business Information Processing, International Conference on Enterprise Design, Operations, and Computing (EDOC): EDOC Workshops, Springer International Publishing, Bozen-Bolzano, Italy, pp. 214-229.
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Ansari, M, Zetterstrom, O, Fonseca, NJG, Quevedo-Teruel, O & Guo, YJ 1970, 'A Lightweight Metalized-Insert Luneburg Lens', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Arnaz, A, Lipman, J & Abolhasan, M 1970, 'A Multi-objective Reinforcement Learning Solution for Handover Optimization in URLLC', 2023 28th Asia Pacific Conference on Communications (APCC), 2023 28th Asia Pacific Conference on Communications (APCC), IEEE.
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Bandara, M, Jiang, Y, Gill, A, Rabhi, F & Beydoun, G 1970, 'An Application Ontology for Reproducibility of Machine Learning Solutions', Australasian Conference on Information Systems, Wellington, New Zealand.

Chen, K, Zhang, JA, Wang, Z & Guo, YJ 1970, 'Development of an Uplink Sensing Demonstrator for Perceptive Mobile Networks', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE.
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Chen, S-L & Guo, YJ 1970, 'Beam-Squint Mitigated Transverse-Slot Leaky-Wave Antenna for Wideband Wireless Communications', 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), IEEE.
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Chen, S-L, Qin, P-Y, Liu, Y & Lin, W 1970, 'Recent Advances and Future Scopes of Multi-Linear Polarization Reconfigurable Antennas', 2023 International Conference on Microwave and Millimeter Wave Technology (ICMMT), 2023 International Conference on Microwave and Millimeter Wave Technology (ICMMT), IEEE.
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Chen, Y, Ding, C, Zhu, H, Liu, Y & Guo, YJ 1970, 'A Dual-Slant-Polarized In-band Full-duplex (IBFD) Antenna System with Four Isolated Channels', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Chu, NH, Nguyen, DN, Hoang, DT, Phan, KT, Dutkiewicz, E, Niyato, D & Shu, T 1970, 'Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning', 2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 1-6.
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This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Specifically, by studying functions of Metaverse applications, we first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications to enhance resource usage efficiency. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision process-based framework and propose an intelligent algorithm that can gradually learn the optimal admission policy to maximize the revenue and resource usage efficiency for the Metaverse service provider and at the same time enhance the Quality-of-Service for Metaverse users. Extensive simulation results show that our proposed approach can achieve up to 120% greater revenue for the Metaverse service providers and up to 178.9% higher acceptance probability for Metaverse application requests than those of other baselines.

Deng, H, Wang, X, Yu, G, Dang, X & Liu, RP 1970, 'A Novel Weights-less Watermark Embedding Method for Neural Network Models', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE.
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Doan, QM, Dinh, TH, Trung, NL, Nguyen, DN, Singh, AK & Lin, C-T 1970, 'Extended Upscale and Downscale Representation with Cascade Arrangement', 2023 IEEE Statistical Signal Processing Workshop (SSP), 2023 IEEE Statistical Signal Processing Workshop (SSP), IEEE.
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Guo, CA, Guo, YJ, Wu, K & Yuan, J 1970, 'A New Technique to Form Steerable Multibeams for Integrated Communications and Sensing', 2023 IEEE International Conference on Communications Workshops (ICC Workshops), 2023 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE.
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Guo, CA, Jay Guo, Y, Zhu, H & Yuanr, J 1970, 'Optimizing Multibeam Feed Networks for Antenna Arrays with Independent Beam Steering and Low Sidelobes', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Guo, YJ 1970, 'A New Circuit-Type Multibem Antenna Array Employing Generalized Joined Coupler Matrix', 2023 IEEE International Symposium On Antennas And Propagation (ISAP), 2023 IEEE International Symposium On Antennas And Propagation (ISAP), IEEE.
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Guo, YJ 1970, 'Phased Multibeam Antennas Employing Generalized Joined Coupler Matrix', 2023 International Workshop on Antenna Technology (iWAT), 2023 International Workshop on Antenna Technology (iWAT), IEEE.
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Hieu, NQ, Chu, NH, Hoang, DT, Nguyen, DN & Dutkiewicz, E 1970, 'A Unified Resource Allocation Framework for Virtual Reality Streaming over Wireless Networks', ICC 2023 - IEEE International Conference on Communications, ICC 2023 - IEEE International Conference on Communications, IEEE.
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Hieu, NQ, Hoang, DT, Nguyen, DN & Dutkiewicz, E 1970, 'Toward BCI-Enabled Metaverse: A Joint Learning and Resource Allocation Approach', GLOBECOM 2023 - 2023 IEEE Global Communications Conference, GLOBECOM 2023 - 2023 IEEE Global Communications Conference, IEEE.
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Huang, W, Liao, X, Qian, Y & Jia, W 1970, 'Learning Hierarchical Semantic Information for Efficient Low-Light Image Enhancement', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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Low-light environments can cause a variety of complex degradation problems, which result in poor visibility in images. As a classical vision task, low-light image enhancement has attracted an increasing interest in the research community. However, the existing methods tend to require a large number of parameters, making them difficult to implement and optimize, especially on resource-constrained devices. In this paper, we mainly focus on the lightweight of the method and propose a novel end-to-end two-stage CNN-ViT architecture (HSINet) to learn hierarchical semantic information (HSI) from low-light images efficiently. The HSINet consists of two stages: the first stage is a CNN-based low-level semantic (LS) Stage, and the second stage is ViT-based high-level semantic (HS) Stage. The LS Stage contains an efficient multi-scale convolution block, MLS Block, for low-level semantic information extraction. The HS stage, on the other hand, aims to learn the high-level semantic features via ViT's excellent global-learning capability. We propose a hierarchical Swin Transformer-based block, HS Block, to gradually enlarge Swin Transformer's window size as the network becomes deeper, to learn hierarchical high-level semantic information. Benefiting from the efficient architecture, our model only contains 0.6M parameters, far fewer than the existing SOTAs. We evaluated the method on three challenging benchmark datasets: LOL, VE-LOL, and MIT-Adobe FiveK, using three popular evaluation metrics. The quantitative and qualitative results both show that the proposed method not only outperforms the state of the arts in terms of PSNR, SSIM, LPIPS, and visual effects, but also with better efficiency.

Huynh, NV, Quang Hieu, N, Chu, NH, Nguyen, DN, Hoang, DT & Dutkiewicz, E 1970, 'Defeating Eavesdroppers with Ambient Backscatter Communications', 2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 1-6.
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Unlike conventional anti-eavesdropping methods that always require additional energy or computing resources (e.g., in friendly jamming and cryptography-based solutions), this work proposes a novel anti-eavesdropping solution that comes with mostly no extra power nor computing resource requirement. This is achieved by leveraging the ambient backscatter technology in which secret information can be transmitted by backscattering it over ambient radio signals. Specifically, the original message at the transmitter is first encoded into two parts: (i) active transmit message and (ii) backscatter message. The active transmit message is then transmitted by using the conventional wireless transmission method while the backscatter message is transmitted by backscattering it on the active transmit signals via an ambient backscatter tag. As the backscatter tag does not generate any active RF signals, it is intractable for the eavesdropper to detect the backscatter message. Therefore, secret information, e.g., a secret key for decryption, can be carried by the backscattered message, making the adversary unable to decode the original message. Simulation results demonstrate that our proposed solution can significantly enhance security protection for communication systems.

Keshavarz, R, Winson, D, Lipman, J, Abolhasan, M & Shariati, N 1970, 'Dual-Band, Slant-Polarized MIMO Antenna Set for Vehicular Communication', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Li, H, Wang, X, Yu, G, Ni, W & Liu, RP 1970, 'A Generative Adversarial Networks-Based Integer Overflow Detection Model for Smart Contracts', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE.
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Li, S, Unanue, IJ & Piccardi, M 1970, 'Improving Machine Translation and Summarization with the Sinkhorn Divergence', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature Switzerland, pp. 149-161.
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Important natural language processing tasks such as machine translation and document summarization have made enormous strides in recent years. However, their performance is still partially limited by the standard training objectives, which operate on single tokens rather than on more global features. Moreover, such standard objectives do not explicitly consider the source documents, potentially affecting their alignment with the predictions. For these reasons, in this paper, we propose using an Optimal Transport (OT) training objective to promote a global alignment between the model’s predictions and the source documents. In addition, we present an original implementation of the OT objective based on the Sinkhorn divergence between the final hidden states of the model’s encoder and decoder. Experimental results over machine translation and abstractive summarization tasks show that the proposed approach has been able to achieve statistically significant improvements across all experimental settings compared to our baseline and other alternative objectives. A qualitative analysis of the results also shows that the predictions have been able to better align with the source sentences thanks to the supervision of the proposed objective.

Liu, Y, Qi, M, Wu, Q, Yang, Y, Li, X & Zhang, J 1970, 'Camera Proxy based Contrastive Learning with Hard Sampling for Unsupervised Person Re-identification', 2023 IEEE International Conference on Multimedia and Expo (ICME), 2023 IEEE International Conference on Multimedia and Expo (ICME), IEEE.
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Mei, G, Poiesi, F, Saltori, C, Zhang, J, Ricci, E & Sebe, N 1970, 'Overlap-guided Gaussian Mixture Models for Point Cloud Registration', 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 4500-4509.
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Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations. However, registering point cloud pairs in the case of partial overlap is still a challenge. This paper proposes a novel overlap-guided probabilistic registration approach that computes the optimal transformation from matched Gaussian Mixture Model (GMM) parameters. We reformulate the registration problem as the problem of aligning two Gaussian mixtures such that a statistical discrepancy measure between the two corresponding mixtures is minimized. We introduce a Transformer-based detection module to detect overlapping regions, and represent the input point clouds using GMMs by guiding their alignment through overlap scores computed by this detection module. Experiments show that our method achieves superior registration accuracy and efficiency than state-of-the-art methods when handling point clouds with partial overlap and different densities on synthetic and real-world datasets. https://github.com/gfmei/ogmm

Mei, G, Tang, H, Huang, X, Wang, W, Liu, J, Zhang, J, Van Gool, L & Wu, Q 1970, 'Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration', 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.
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Mian, A, Gill, AQ & Sharma, N 1970, 'Towards the Development of a Security Threat Identification Framework for UAV Information Infrastructure.', SmartIoT, IEEE, pp. 305-309.

Ngo, QT, Jayawickrama, B, He, Y & Dutkiewicz, E 1970, 'Machine Learning-Based Cyclostationary Spectrum Sensing in Cognitive Dual Satellite Networks', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE.
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Nguyen, HM, Chu, NH, Nguyen, DN, Hoang, DT, Hà, MH & Dutkiewicz, E 1970, 'Optimal Privacy Preserving in Wireless Federated Learning Over Mobile Edge Computing', ICC 2023 - IEEE International Conference on Communications, ICC 2023 - IEEE International Conference on Communications, IEEE.
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Nguyen, V-D, Vu, TX, Nguyen, NT, Nguyen, DC, Juntti, M, Luong, NC, Hoang, DT, Nguyen, DN & Chatzinotas, S 1970, 'Enabling Intelligent Traffic Steering in A Hierarchical Open Radio Access Network', GLOBECOM 2023 - 2023 IEEE Global Communications Conference, GLOBECOM 2023 - 2023 IEEE Global Communications Conference, IEEE.
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Song, L, Qin, P, Du, J & Guo, YJ 1970, 'Multi-beam Conformal Transmitarray Synthesis for Advanced Wireless Systems', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Song, L, Qin, P, Zhang, T, Du, J & Jay Guo, Y 1970, 'High-Efficiency Multi-Beam GRIN Lens with 2-D Wide-Angle Coverages', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE, Melbourne, Australia, pp. 51-52.
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Multi beam gradient index GRIN lenses are investigated to enable high aperture efficiencies and 2 D wide angular coverages Metasurface composed of engineered elements with varied refractive indices is employed to implement the lens Innovative methodologies are developed to calculate refractive index distributions on the lens as well as the corresponding feed positions for multiple beams A three metal layer element is developed to enable the variation of effective refractive index Microstrip patch array antennas are designed as feed sources Thirteen feed arrays are arranged on two focal arcs in xoz and yoz planes A GRIN lens prototype is constructed radiating multiple beams in a wide range of 45 in two orthogonal planes with low scanning losses The peak realized gain is 21 8 dBi with an aperture efficiency of 62 8 The operating bandwidth is 22 2 from 12 GHz to 15 GHz

Sun, S, Ding, C & Jay Guo, Y 1970, 'An Electromagnetically Transparent Dipole for Cross-Band Scattering and Coupling Suppression', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE, pp. 57-58.
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In this paper, an electromagnetically transparent dipole working at the low-band (LB, 1.7-2.5 GHz) is proposed to simultaneously suppress the cross-band scattering and coupling to the high-band (HB, 4.4-5.0 GHz) antenna in a dual-band array. Dual-functional patches with vias are loaded on radiating arms of the LB dipole, which can introduce reversed induced-currents at the HB to restore the deteriorated HB radiation patterns and improve cross-band isolations at the HB. After the loading, HB patterns are well recovered, showing great consistency with the patterns of the HB dipoles operating alone. The transmission coefficients between the LB and HB ports are also greatly reduced.

Sun, S-Y, Ding, C & Guo, YJ 1970, 'Holistic Interaction Suppression in Dual-Band Antenna Array', 2023 IEEE International Symposium On Antennas And Propagation (ISAP), 2023 IEEE International Symposium On Antennas And Propagation (ISAP), IEEE.
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Tang, H, Zhao, Y, Jiang, Y, Gan, Z & Wu, Q 1970, 'Class-Aware Contextual Information for Semantic Segmentation', ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE.
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Wang, X, Qin, P-Y, Guo, YJ & Gupta, K 1970, 'Reconfigurable Dual-Layer Unit Cell Based Beam Steering Transmitarray', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE, Florence, Italy, pp. 1-4.
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A beam steering transmitarray utilizing reconfigurable dual layer unit cells is presented in this paper Two PIN diodes are used on each array unit cell to achieve a 1 bit phase change with a high transmission Compared to other electronically reconfigurable transmitarrays employing multilayer unit cells with metal vias the developed transmitarray has a much simpler configuration which is beneficial to larger aperture designs in high frequency bands To validate the unit cell design concept a transmitarray prototype at 13 GHz is designed The measured peak gain is 18 4 dBi and 2 D beam steering performances are 50 and 40 in E and H planes respectively

Wang, X, Qin, P-Y, Jin, R & Guo, YJ 1970, 'Wideband Conformal Transmitarray Employing Tightly Coupled Huygens Element at E Band', 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), IEEE, pp. 485-486.
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A new method to achieve wideband conformal transmitarrays is presented by employing tightly coupled Huygens element in this paper. The element is composed of five pairs of metallic strips which are printed on two sides of a dielectric substrate. The tightly coupled elements can achieve a high transmission and a nearly full phase coverage in a wide bandwidth from 71 to 87 GHz. To further analyze the element, equivalent circuit models are developed. Good agreement is achieved between circuit and full-wave simulations. A cylindrical conformal transmitarray at 78 GHz is designed, fabricated and measured. The measured results have good agreement with the simulated ones. The measured peak realized gain is 26.6 dBi with an aperture efficiency of 37.2%. The measured 3-dB gain bandwidth is 20.4% from 71 to 87 GHz, which can fully cover the E-band spectrum.

Wang, Y, Ren, Y, Zhang, H, Li, H & Zhang, J 1970, 'An Intrusion Detection Method Based on Hash Function for Industrial Cloud Data', 2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS), 2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS), IEEE.
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Wen, Y, Qin, P-Y & Guo, YJ 1970, 'A Low Profile Modulated Metasurface Antenna for Multi-Beam Applications', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Wen, Y, Qin, P-Y & Jay Guo, Y 1970, 'A Multi-Beam Antenna Based on Modulated Metasurface', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE, Melbourne, Australia, pp. 55-56.
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In this paper modulated metasurface based multibeam antenna is presented The impedance superposition method of all impedance modulations for different beams is utilized in the design The current reported multi beam modulated metasurface has a very small number of beams due to the mutual interference of different impedance modulations It is found that the source locations for each beam play a key role in interference suppression Instead of using the time consuming full wave simulation based optimization in this paper the optimal source locations are obtained by using the aperture fields calculated from the zeroth order approximation of the currents on the metasurface A five beam modulated metasurface antenna has been designed providing an angular coverage up to 30 The peak realized gain for the broadside beam is 21 3 dBi at 14 30 GHz

Weththasinghe, K, Clark, N, Ngo, QT, Jayawickrama, B, He, Y, Dutkiewicz, E & Liu, RP 1970, 'L-Band Spectral Opportunities for Cognitive GEO-LEO Dual Satellite Networks', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE.
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Wu, K, Zhang, JA, Huang, X & Guo, YJ 1970, 'A Low-Complexity CSI-Based Wifi Sensing Scheme for LoS-Dominant Scenarios', ICC 2023 - IEEE International Conference on Communications, ICC 2023 - IEEE International Conference on Communications, IEEE.
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Wu, Y, Yu, L & Zhang, J 1970, 'Adaptive Local Feature Matching for Few-shot Fine-grained Image Recognition', 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE.
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Xu, W, Huang, H, Cheng, M, Yu, L, Wu, Q & Zhang, J 1970, 'Masked Cross-image Encoding for Few-shot Segmentation', 2023 IEEE International Conference on Multimedia and Expo (ICME), 2023 IEEE International Conference on Multimedia and Expo (ICME), IEEE.
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Yang, S, Xu, Z, Wang, K, You, Y, Yao, H, Liu, T & Xu, M 1970, 'BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency', 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Vancouver Convention Center.
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Yu, L, Xu, W, Wu, Q & Zhang, J 1970, 'Automated Flock Density and Activity Recognition for Welfare Monitoring on Commercial Egg Farms', 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), IEEE.
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Zeng, FC, Ding, C & Guo, YJ 1970, 'A Polarization-Mixed Antenna Array with Wide Beamwidth and Orthogonal Polarizations', 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), IEEE.
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Zhang, H, Huang, X & Zhang, JA 1970, 'Fine Doppler Resolution Channel Estimation and Offset Gradient Descent Equalization for OTFS Transmission over Doubly Selective Channels', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE.
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Zhang, T, Zhang, H, Huang, X, Suzuki, H, Pathikulangara, J, Smart, K, Du, J & Guo, JY 1970, 'Demonstration of a 245 GHz Real-Time Wireless Communication link with 30 Gbps Data Rate', 2023 48th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), 2023 48th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), IEEE.
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Zhang, T, Zhang, H, Zhu, H, Huang, X, Suzuki, HH, Pathikulangara, J, Smart, KW, Du, J & Guo, JY 1970, 'Demonstration of a 245 GHz Real-Time Wireless Communication link', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE.
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Zhang, Z, Wang, Y, Yu, G, Wang, X, Zhang, M, Ni, W & Liu, RP 1970, 'A Community-based Strategy for Blockchain Sharding: Enabling More Budget-friendly Transactions', 2023 IEEE International Conference on Blockchain (Blockchain), 2023 IEEE International Conference on Blockchain (Blockchain), IEEE.
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Zhao, Y, Tang, H, Jiang, Y, A, Y, Wu, Q & Wang, J 1970, 'Parameter-Efficient Vision Transformer with Linear Attention', 2023 IEEE International Conference on Image Processing (ICIP), 2023 IEEE International Conference on Image Processing (ICIP), IEEE.
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Zhu, H & Jay Guo, Y 1970, 'Single-Ended-to-Balanced Hybrid Coupler for In-Band Full-Duplex Transceivers', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE.
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Zhu, H, Guo, CA & Jay Guo, Y 1970, 'Planar Beamforming Networks for Producing Multiple Independently Steerable Beams', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Tovey, A, Heydon, G, Phansalkar, A, Ruoso, L-E, Alexandra, B, Qureshi Atif, M, Gill, A, Siddiqui, S, Perry, C, Liu, B, Runcie, P, McIntyre, E, Goodman, N, Vardoulakis, S, Hu, T, Izzo, K, Surawski, N, Kulkarni, Y, Liyanage, L & Barns, S Operational Network of Air Quality Impact Resources (OPENAIR) 2023, The OPENAIR Best Practice Guide for smart air quality monitoring, pp. 1-9, Australia.
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The OPENAIR Best Practice Guide for smart air quality monitoring has been developed to help local governments implement smart air quality monitoring projects. It has been developed through collaboration between five NSW universities, the NSW Government, and the NSW Smart Sensing Network (NSSN). It contains world-leading best practice guidance for smart air quality monitoring and we believe it to be the most comprehensively broad and simultaneously in-depth practical resource on this topic in the world.The Guide is divided into sections that reflect the six stages of the OPENAIR Impact Planning Cycle. Each section is organised into topic areas, with a suite of associated resources.Each factsheet, Best Practice Guide chapter, and supplementary resource is available as an individual download. You can also download the entire Best Practice Guide as a single PDF document, with all chapters combined.

ashtarinakhaei, S, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2023, 'A Location-Based Multi-Hop Routing Protocol for Future Wireless Cellular Networks', Elsevier BV.
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Chu, NH, Huynh, NV, Nguyen, DN, Hoang, DT, Gong, S, Shu, T, Dutkiewicz, E & Phan, KT 2023, 'Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications'.

Chu, NH, Nguyen, DN, Hoang, DT, Phan, KT, Dutkiewicz, E, Niyato, D & Shu, T 2023, 'Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning'.

Dinh, PV, Nguyen, QU, Hoang, DT, Nguyen, DN, Bao, SP & Dutkiewicz, E 2023, 'Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems'.

Gill, A 2023, 'Adaptive Architecture for Data, Analytics, and AI: Government's Navigation of Digital Disruption', 9th Annual FST Government Australia Summit 2023, Canberra, Australia.

Gill, A 2023, 'Adaptive Data Architecture For Responsible & Safe AI Adoption in Government: Reflections & Learnings from 2023', Public Sector Network.

Gill, A 2023, 'Adaptive Data Architecture For Responsible & Safe AI Adoption in Government: Reflections & Learnings from 2023', Public Sector Network.

Gill, A 2023, 'ADAPTIVE ENTERPRISE ARCHITECTURE WORKSHOP FOR DECISION MAKERS'.

Gill, A 2023, 'Data Sharing: Architecture, Patterns and Technology Solutions'.

Gill, A 2023, 'The ArcOps: Connected Enterprise Architecture-Operations Pipeline', Enterprise Architecture Professional Journal.

Gill, A & Fitzgibbon, T 2023, 'Why trusted digital identity verification platform is critical for supporting student lifecycle journey?', Australasian Higher Education Cyber Security Service, Canberra, Australia.

Hassan, MA, Jamshidi, MB, Manh, BD, Chu, NH, Nguyen, C-H, Hieu, NQ, Nguyen, CT, Hoang, DT, Nguyen, DN, Huynh, NV, Alsheikh, MA & Dutkiewicz, E 2023, 'Enabling Technologies for Web 3.0: A Comprehensive Survey'.

Hieu, NQ, Hoang, DT, Nguyen, DN & Alsheikh, MA 2023, 'Reconstructing Human Pose from Inertial Measurements: A Generative Model-based Compressive Sensing Approach'.

Hieu, NQ, Hoang, DT, Nguyen, DN, Nguyen, V-D, Xiao, Y & Dutkiewicz, E 2023, 'Enhancing Immersion and Presence in the Metaverse with Over-the-Air Brain-Computer Interface'.

Hoang, TD, Huang, X & Qin, P 2023, 'Low-Complexity Compressed Sensing-Aided Coherent Direction-of-Arrival Estimation for Large-Scale Lens Antenna Array', Institute of Electrical and Electronics Engineers (IEEE).
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Hoang, TD, Huang, X & Qin, P 2023, 'Low-Complexity Compressed Sensing-Aided Coherent Direction-of-Arrival Estimation for Large-Scale Lens Antenna Array', Institute of Electrical and Electronics Engineers (IEEE).
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Huynh, NV, Wang, J, Du, H, Hoang, DT, Niyato, D, Nguyen, DN, Kim, DI & Letaief, KB 2023, 'Generative AI for Physical Layer Communications: A Survey'.

Khoa, TV, Son, DH, Nguyen, C-H, Hoang, DT, Nguyen, DN, Trung, NL, Quynh, TTT, Hoang, T-M, Ha, NV, Dutkiewicz, E & Alsheikh, MA 2023, 'Securing Blockchain Systems: A Novel Collaborative Learning Framework to Detect Attacks in Transactions and Smart Contracts'.

Kumar, A, Esmaili, N & Piccardi, M 2023, 'The Contextualized Regressive Topic Model', Elsevier BV.
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Le, M, Hoang, DT, Nguyen, DN, Hwang, W-J & Pham, Q-V 2023, 'Wirelessly Powered Federated Learning Networks: Joint Power Transfer, Data Sensing, Model Training, and Resource Allocation'.

Liu, J, Wu, K, Su, T & Zhang, JA 2023, 'Practical Frequency-Hopping MIMO Joint Radar Communications: Design and Experiment'.

Luu, MN, Nguyen, M-D, Bedeer, E, Nguyen, VD, Hoang, DT, Nguyen, DN & Pham, Q-V 2023, 'Sample-Driven Federated Learning for Energy-Efficient and Real-Time IoT Sensing'.

Maier, M & Hoang, DT 2023, '6G and Onward to Next G: the Road to the Multiverse', Institute of Electrical and Electronics Engineers (IEEE), pp. 20-20.
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Nguyen, CT, Hoang, DT, Nguyen, DN, Xiao, Y, Niyato, D & Dutkiewicz, E 2023, 'MetaShard: A Novel Sharding Blockchain Platform for Metaverse Applications'.

Nguyen, V-D, Vu, TX, Nguyen, NT, Nguyen, DC, Juntti, M, Luong, NC, Hoang, DT, Nguyen, DN & Chatzinotas, S 2023, 'Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework'.

Tuan, BM, Nguyen, DN, Trung, NL, Nguyen, V-D, Huynh, NV, Hoang, DT, Krunz, M & Dutkiewicz, E 2023, 'Securing MIMO Wiretap Channel with Learning-Based Friendly Jamming under Imperfect CSI'.

Unanue, IJ, Haffari, G & Piccardi, M 2023, 'T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification'.

Xiao, Y, Xia, R, Li, Y, Shi, G, Nguyen, DN, Hoang, DT, Niyato, D & Krunz, M 2023, 'Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach'.

Xiao, Y, Zhang, X, Shi, G, Krunz, M, Nguyen, DN & Hoang, DT 2023, 'Time-sensitive Learning for Heterogeneous Federated Edge Intelligence', arXiv.
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Zhu, HY, Hieu, NQ, Hoang, DT, Nguyen, DN & Lin, C-T 2023, 'A Human-Centric Metaverse Enabled by Brain-Computer Interface: A Survey'.

Zhu, W, Tuan, HD, Dutkiewicz, E, Fang, Y & Hanzo, L 2023, 'Low-Complexity Pareto-Optimal 3D Beamforming for the Full-Dimensional Multi-User Massive MIMO Downlink'.