Chakraborty, S, Milner, LE, Zhu, X, Sevimli, O, Parker, AE & Heimlich, MC 2021, 'An Edge-Coupled Marchand Balun with Partial Ground for Excellent Balance in 0.13 μm SiGe Technology', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 1, pp. 226-230.
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© 2004-2012 IEEE. An edge-coupled meandered three-coupled-line Marchand balun with a partial ground plane implemented in 0.13 μ {m} SiGe Bi-CMOS technology is presented in this brief. The balance performance of the designed balun is significantly improved by creating a 'no ground plane' beneath the coupled-line structure, which is demonstrated by simulating two baluns: one with a partial ground and the other with a solid ground underneath. The measured amplitude and phase imbalances are less than 0.4 dB and 2.5°, across the 3-dB bandwidth from 21.5 to 95 GHz, surpassing previously reported results of edge-coupled Marchand baluns. The balun occupies 230 μ \text{m}\,\,×370\,\,μ {m}.
Gao, G, Yu, Y, Xie, J, Yang, J, Yang, M & Zhang, J 2021, 'Constructing multilayer locality-constrained matrix regression framework for noise robust face super-resolution', PATTERN RECOGNITION, vol. 110.
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Ge, Z, Chen, L, Yang, L, Gomez-Garcia, R & Zhu, X 2021, 'On-Chip Millimeter-Wave Integrated Absorptive Bandstop Filter in (Bi)-CMOS Technology', IEEE ELECTRON DEVICE LETTERS, vol. 42, no. 1, pp. 114-117.
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Rahman, ML, Zhang, JA, Huang, X, Guo, YJ & Lu, Z 2021, 'Gaussian-Mixture-Model Based Clutter Suppression in Perceptive Mobile Networks', IEEE COMMUNICATIONS LETTERS, vol. 25, no. 1, pp. 152-156.
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Sang, L, Xu, M, Qian, S & Wu, X 2021, 'Knowledge graph enhanced neural collaborative recommendation', Expert Systems with Applications, vol. 164.
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© 2020 Existing neural collaborative filtering (NCF) recommendation methods suffer from severe sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem. However, pure NCF models can hardly model the high-order connectivity in KG, and ignores complex pairwise correlations between user/item embedding dimensions. To address these problems, we propose a novel Knowledge graph enhanced Neural Collaborative Recommendation (K-NCR) framework, which effectively combines user–item interaction information and auxiliary knowledge information for recommendation task into three parts: (1) For items, the proposed propagating model learns the representation of item entity. It recursively aggregates information from its multi-hop neighbours in KG, and employs an attention mechanism to discriminate the importance of the relation type to mine users’ potential preferences. (2) For users, another heterogeneous attention weights are leveraged to strengthen the embedding learning of users. (3) The user and item embeddings are then fed into a newly designed two-dimensional interaction map with convolutional hidden layers to model the complex pairwise correlations between their embedding dimensions explicitly. Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our K-NCR framework.
Saputra, YM, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2021, 'A Novel Mobile Edge Network Architecture with Joint Caching-Delivering and Horizontal Cooperation', IEEE Transactions on Mobile Computing, vol. 20, no. 1, pp. 19-31.
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Mobile edge caching/computing (MEC) has been emerging as a promising paradigm to provide ultra-high rate, ultra-reliable, and/or low-latency communications in future wireless networks. In this paper, we introduce a novel MEC network architecture that leverages the optimal joint caching-delivering with horizontal cooperation among mobile edge nodes (MENs). To that end, we first formulate the content-access delay minimization problem by jointly optimizing the content caching and delivering decisions under various network constraints (e.g., network topology, storage capacity and users’ demands at each MEN). However, the strongly mutual dependency between the decisions makes the problem a nested dual optimization that is proved to be NP-hard. To deal with it, we propose a novel transformation method to transform the nested dual problem to an equivalent mixed-integer nonlinear programming (MINLP) optimization problem. Then, we design a centralized solution using an improved branch-and-bound algorithm with the interior-point method to find the joint caching and delivering policy which is within 1 percent of the optimal solution. Since the centralized solution requires the full network topology and information from all MENs, to make our solution scalable, we develop a distributed algorithm which allows each MEN to make its own decisions based on its local observations. Extensive simulations demonstrate that the proposed solutions can reduce the total average delay for the whole network up to 40 percent compared with other current caching policies. Furthermore, the proposed solutions also increase the cache hit ratio for the network up to 4 times, thereby dramatically reducing the traffic load on the backhaul network
Sun, Z, Yao, Y, Xiao, J, Zhang, L, Zhang, J & Tang, Z 2021, 'Exploiting textual queries for dynamically visual disambiguation', PATTERN RECOGNITION, vol. 110.
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You, X, Wang, CX, Huang, J, Gao, X, Zhang, Z, Wang, M, Huang, Y, Zhang, C, Jiang, Y, Wang, J, Zhu, M, Sheng, B, Wang, D, Pan, Z, Zhu, P, Yang, Y, Liu, Z, Zhang, P, Tao, X, Li, S, Chen, Z, Ma, X, Chih-Lin, I, Han, S, Li, K, Pan, C, Zheng, Z, Hanzo, L, Shen, XS, Guo, YJ, Ding, Z, Haas, H, Tong, W, Zhu, P, Yang, G, Wang, J, Larsson, EG, Ngo, HQ, Hong, W, Wang, H, Hou, D, Chen, J, Chen, Z, Hao, Z, Li, GY, Tafazolli, R, Gao, Y, Poor, HV, Fettweis, GP & Liang, YC 2021, 'Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts', Science China Information Sciences, vol. 64, no. 1.
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© 2020, The Author(s). The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requireme...