Publications-JZ Group

TCOM	[1]. Jun Zhao. “On secure communication in sensor networks under q-composite key predistribution with unreliable links,” IEEE Transactions on Communications (TCOM), volume 70, issue 2, pages 1085-1095, February 2022. DOI Bibtex PDF
TCOM	[2]. Yang Liu, Jun Zhao, Ming Li, Qingqing Wu, “Intelligent Reflecting Surface Aided MISO Uplink Communication Network: Feasibility and Power Minimization for Perfect and Imperfect CSI”, IEEE Transactions on Communications (TCOM), 2021. DOI Bibtex PDF Bibtex @ARTICLE{9270033, author={Liu, Yang and Zhao, Jun and Li, Ming and Wu, Qingqing}, journal={IEEE Transactions on Communications}, title={Intelligent Reflecting Surface Aided MISO Uplink Communication Network: Feasibility and Power Minimization for Perfect and Imperfect CSI}, year={2021}, volume={69}, number={3}, pages={1975-1989}, doi={10.1109/TCOMM.2020.3040404}}
TCOM	[3]. Huimei Han, Jun Zhao, Wenchao Zhai, Zehui Xiong, Dusit Niyato, Marco Di Renzo, Quoc-Viet Pham, Weidang Lu, and Kwok-Yan Lam. "Reconfigurable intelligent surface aided power control for physical-layer broadcasting." IEEE Transactions on Communications 69, no. 11 (2021): 7821-7836. DOI Bibtex PDF Bibtex @ARTICLE{9514563, author={Han, Huimei and Zhao, Jun and Zhai, Wenchao and Xiong, Zehui and Niyato, Dusit and Di Renzo, Marco and Pham, Quoc-Viet and Lu, Weidang and Lam, Kwok-Yan}, journal={IEEE Transactions on Communications}, title={Reconfigurable Intelligent Surface Aided Power Control for Physical-Layer Broadcasting}, year={2021}, volume={69}, number={11}, pages={7821-7836}, doi={10.1109/TCOMM.2021.3104871}}
TCOM	[4]. Yulan Gao, Chao Yong, Zehui Xiong, Jun Zhao, Yue Xiao, and Dusit Niyato, “Reflection Resource Management for Intelligent Reflecting Surface Aided Wireless Networks”, IEEE Transactions on Communications (TCOM), 2021. DOI Bibtex PDF Bibtex @ARTICLE{9467371, author={Gao, Yulan and Yong, Chao and Xiong, Zehui and Zhao, Jun and Xiao, Yue and Niyato, Dusit}, journal={IEEE Transactions on Communications}, title={Reflection Resource Management for Intelligent Reflecting Surface Aided Wireless Networks}, year={2021}, volume={69}, number={10}, pages={6971-6986}, doi={10.1109/TCOMM.2021.3093312}}
TCOM	[5]. Meng Hua, Qingqing Wu, Derrick Wing Kwan Ng, Jun Zhao, Luxi Yang. “Intelligent Reflecting Surface-Aided Joint Processing Coordinated Multipoint Transmission”, IEEE Transactions on Communications (TCOM), 2020. DOI Bibtex PDF Bibtex @ARTICLE{9279253, author={Hua, Meng and Wu, Qingqing and Ng, Derrick Wing Kwan and Zhao, Jun and Yang, Luxi}, journal={IEEE Transactions on Communications}, title={Intelligent Reflecting Surface-Aided Joint Processing Coordinated Multipoint Transmission}, year={2021}, volume={69}, number={3}, pages={1650-1665}, doi={10.1109/TCOMM.2020.3042275}}
JSAC	[6]. Helin Yang, Jun Zhao, Zehui Xiong, Kwok-Yan Lam, Sumei Sun, and Liang Xiao, “Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management", IEEE Journal on Selected Areas in Communications (JSAC), volume 39, issue 10, pages 3144-3159 (16 pages), 2021. DOI Bibtex PDF Bibtex @ARTICLE{9453811, author={Yang, Helin and Zhao, Jun and Xiong, Zehui and Lam, Kwok-Yan and Sun, Sumei and Xiao, Liang}, journal={IEEE Journal on Selected Areas in Communications}, title={Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management}, year={2021}, volume={39}, number={10}, pages={3144-3159}, doi={10.1109/JSAC.2021.3088655}}
TWC	[7]. Weiheng Jiang, Yu Zhang, Jun Zhao, Zehui Xiong, Zhiguo Ding, "Joint Transmit Precoding and Reflect Beamforming Design for IRS-Assisted MIMO Cognitive Radio Systems," in IEEE Transactions on Wireless Communications, 2021. DOI Bibtex PDF Bibtex @ARTICLE{9599553, author={Jiang, Weiheng and Zhang, Yu and Zhao, Jun and Xiong, Zehui and Ding, Zhiguo}, journal={IEEE Transactions on Wireless Communications}, title={Joint Transmit Precoding and Reflect Beamforming Design for IRS-Assisted MIMO Cognitive Radio Systems}, year={2021}, volume={}, number={}, pages={1-1}, doi={10.1109/TWC.2021.3122959}}
TWC	[8]. Helin Yang, Jun Zhao, Zehui Xiong, Kwok-Yan Lam, Qingqing Wu, and Liang Xiao, “Distributed Deep Reinforcement Learning Based Spectrum and Power Allocation for Heterogeneous Networks", in IEEE Transactions on Wireless Communications (TWC), 2022. DOI Bibtex PDF
TWC	[9]. Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Liang Xiao, and Qingqing Wu, "Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications," in IEEE Transactions on Wireless Communications (TWC), 2020. DOI Bibtex PDF
TWC	[10]. Yue Xiu, Jun Zhao, Wei Sun, Marco Di Renzo, Guan Gui, Zhongpei Zhang and Ning Wei,, “Reconfigurable Intelligent Surfaces Aided mmWave NOMA: Joint Power Allocation, Phase Shifts, and Hybrid Beamforming Optimization", in IEEE Transactions on Wireless Communications (TWC), volume 20, issue 12, pages 8393-8409, December 2021. DOI Bibtex PDF Bibtex @ARTICLE{9472958, author={Xiu, Yue and Zhao, Jun and Sun, Wei and Renzo, Marco Di and Gui, Guan and Zhang, Zhongpei and Wei, Ning}, journal={IEEE Transactions on Wireless Communications}, title={Reconfigurable Intelligent Surfaces Aided mmWave NOMA: Joint Power Allocation, Phase Shifts, and Hybrid Beamforming Optimization}, year={2021}, volume={20}, number={12}, pages={8393-8409}, doi={10.1109/TWC.2021.3092597}}
TWC	[11]. Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Chau Yuen, and Ruilong Deng, “Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications”, IEEE Transactions on Wireless Communications (TWC), 2020. DOI Bibtex PDF Bibtex @ARTICLE{9311792, author={Yang, Helin and Xiong, Zehui and Zhao, Jun and Niyato, Dusit and Yuen, Chau and Deng, Ruilong}, journal={IEEE Transactions on Wireless Communications}, title={Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications}, year={2021}, volume={20}, number={5}, pages={2977-2990}, doi={10.1109/TWC.2020.3046262}}
TWC	[12]. Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Qingqing Wu, and H. Vincent Poor and Massimo Tornatore “Intelligent Reflecting Surface Assisted Anti-Jamming Communications: A Fast Reinforcement Learning Approach”, IEEE Transactions on Wireless Communications (TWC), volume 20, issue 3, pages 1963-1974, March 2021. DOI Bibtex PDF Bibtex @ARTICLE{9264659, author={H. {Yang} and Z. {Xiong} and J. {Zhao} and D. {Niyato} and Q. {Wu} and H. V. {Poor} and M. {Tornatore}}, journal={IEEE Transactions on Wireless Communications (TWC)}, title={Intelligent Reflecting Surface Assisted Anti-Jamming Communications: A Fast Reinforcement Learning Approach}, year={2020}, volume={}, number={}, pages={1-1}, doi={10.1109/TWC.2020.3037767} }
TIFS	[13]. Ziyao Liu, Jiale Guo, Kwok-Yan Lam, and Jun Zhao. Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learning. IEEE Transactions on Information Forensics & Security, Sept 2022. DOI Bibtex PDF
TIFS	[14]. Hangyu Tian, Kaiping Xue, Shaohua Li, Jiangqing Liu, Jun Zhao and David S.L. Wei,. "Enabling Cross-chain Transactions: A Decentralized Cryptocurrency Exchange Protocol", IEEE Transactions on Information Forensics & Security (TIFS), 2021. DOI Bibtex PDF Bibtex @ARTICLE{9478888, author={Tian, Hangyu and Xue, Kaiping and Luo, Xinyi and Li, Shaohua and Xu, Jie and Liu, Jianqing and Zhao, Jun and Wei, David S. L.}, journal={IEEE Transactions on Information Forensics and Security}, title={Enabling Cross-Chain Transactions: A Decentralized Cryptocurrency Exchange Protocol}, year={2021}, volume={16}, number={}, pages={3928-3941}, doi={10.1109/TIFS.2021.3096124}}
TIFS	[15]. Jun Zhao. "On resilience and connectivity of secure wireless sensor networks under node capture attacks," IEEE Transactions on Information Forensics and Security (TIFS), volume 12, issue 3, pages 557-571 (15 pages), March 2017. DOI Bibtex PDF
TMC	[16]. Zehui Xiong, Jun Zhao, Yang Zhang, Dusit Niyato, and Junshan Zhang, "Contract Design in Hierarchical Game for Sponsored Content Service Market," IEEE Transactions on Mobile Computing (TMC), volume 20, issue 9, pages 2763-2778, September 2021. DOI Bibtex PDF
TVT	[17]. Deyou Zhang, Jun Zhao, Ang Li, Jun Li, Branka Vucetic, Yonghui Li, "Mobile User Trajectory Tracking for IRS Enabled Wireless Networks," in IEEE Transactions on Vehicular Technology, volume 70, issue 8, pages 8331-8336, Aug. 2021. DOI Bibtex PDF Bibtex @ARTICLE{9479771, author={Zhang, Deyou and Zhao, Jun and Li, Ang and Li, Jun and Vucetic, Branka and Li, Yonghui}, journal={IEEE Transactions on Vehicular Technology}, title={Mobile User Trajectory Tracking for IRS Enabled Wireless Networks}, year={2021}, volume={70}, number={8}, pages={8331-8336}, doi={10.1109/TVT.2021.3095943}}
TVT	[18]. Weiheng Jiang, Bolin Chen, Jun Zhao, Zehui Xiong and Zhiguo Ding, "Joint Active and Passive Beamforming Design for the IRS-Assisted MIMOME-OFDM Secure Communications," in IEEE Transactions on Vehicular Technology, volume 70, no. 10, pages. 10369-10381, 2021. DOI Bibtex PDF Bibtex @ARTICLE{9520295, author={Jiang, Weiheng and Chen, Bolin and Zhao, Jun and Xiong, Zehui and Ding, Zhiguo}, journal={IEEE Transactions on Vehicular Technology}, title={Joint Active and Passive Beamforming Design for the IRS-Assisted MIMOME-OFDM Secure Communications}, year={2021}, volume={70}, number={10}, pages={10369-10381}, doi={10.1109/TVT.2021.3106351}}
TVT Best Paper Award	[19]. Jiawen Kang, Zehui Xiong, Dusit Niyato, Dongdong Ye, Dong In Kim, and Jun Zhao. "Towards Secure Blockchain-enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory," IEEE Transactions on Vehicular Technology (TVT), 68(3), 2906-2920, 2019. Best Paper Award (IEEE Transaction Paper) by IEEE Vehicular Society (VTS) Singapore Chapter DOI Bibtex PDF Bibtex @article{kang2019toward, title={Toward secure blockchain-enabled Internet of Vehicles: Optimizing consensus management using reputation and contract theory}, author={Kang, Jiawen and Xiong, Zehui and Niyato, Dusit and Ye, Dongdong and Kim, Dong In and Zhao, Jun}, journal={IEEE Transactions on Vehicular Technology (TVT)}, volume={68}, number={3}, pages={2906--2920}, year={2019}, publisher={IEEE} }
TVT	[20]. Sun Mao, Lei Liu, Ning Zhang, Mianxiong Dong, Jun Zhao, Jinsong Wu, and Victor C. M. Leung. Reconfigurable Intelligent Surface-assisted Secure Mobile Edge Computing Networks. IEEE Transactions on Vehicular Technology, 2022. DOI Bibtex PDF
TVT	[21]. Huimei Han, Lushun Fang, Weidang Lu, Kaikai Chi, Wenchao Zhai, Jun Zhao, "A Novel Grant-Based Pilot Access Scheme for Crowded Massive MIMO Systems," in IEEE Transactions on Vehicular Technology, volume 70, issue 10, pages 11111-11115, Oct. 2021. DOI Bibtex PDF Bibtex @ARTICLE{9535251, author={Han, Huimei and Fang, Lushun and Lu, Weidang and Chi, Kaikai and Zhai, Wenchao and Zhao, Jun}, journal={IEEE Transactions on Vehicular Technology}, title={A Novel Grant-Based Pilot Access Scheme for Crowded Massive MIMO Systems}, year={2021}, volume={70}, number={10}, pages={11111-11115}, doi={10.1109/TVT.2021.3111627}}
TVT	[22]. Xiaoying Liu, Kechen Zheng, Jun Zhao, Xiao-Yang Liu, Xinbing Wang, and Xin Di. “Information-centric networks with correlated mobility,” IEEE Transactions on Vehicular Technology (TVT), volume 66, issue 5, pages 4256–4270 (15 pages), May 2017. DOI Bibtex PDF
TNSE	[23]. Zehui Xiong, Jun Zhao, Dusit Niyato, Ruilong Deng, and Junshan Zhang, "Reward optimization for content providers with mobile data subsidization: A hierarchical game approach", IEEE Transactions on Network Science and Engineering (TNSE), 2021. DOI Bibtex PDF Bibtex @article{article, author = {Xiong, Zehui and Zhao, Jun and Niyato, Dusit and Deng, Ruilong and Zhang, Junshan}, year = {2020}, month = {08}, pages = {1-1}, title = {Reward Optimization for Content Providers with Mobile Data Subsidization: A Hierarchical Game Approach}, volume = {PP}, journal = {IEEE Transactions on Network Science and Engineering (TNSE)}, doi = {10.1109/TNSE.2020.3016963} }
T-ITS	[24]. Huizi Xiao, Jun Zhao, Qingqi Pei, Jie Feng, Lei Liu, Weisong Shi, "Vehicle Selection and Resource Optimization for Federated Learning in Vehicular Edge Computing" in IEEE Transactions on Intelligent Transportation Systems, 2021. DOI Bibtex PDF Bibtex @ARTICLE{9507054, author={Xiao, Huizi and Zhao, Jun and Pei, Qingqi and Feng, Jie and Liu, Lei and Shi, Weisong}, journal={IEEE Transactions on Intelligent Transportation Systems}, title={Vehicle Selection and Resource Optimization for Federated Learning in Vehicular Edge Computing}, year={2021}, volume={}, number={}, pages={1-15}, doi={10.1109/TITS.2021.3099597}}
TFS	[25]. Cong Wang, Witold Pedrycz, ZhiWu Li, MengChu Zhou and Jun Zhao. "Residual-Sparse Fuzzy C-Means Clustering Incorporating Morphological Reconstruction and Wavelet frames", IEEE Transactions on Fuzzy Systems (TFS), 2020. DOI Bibtex PDF
TSC	[26]. Mengmeng Yang, Ivan Tjuawinata, Lam Kwok-Yan, Jun Zhao, Lin Sun, “Secure Hot Path Crowdsourcing with Local Differential Privacy under Fog Computing Architecture”, IEEE Transactions on Services Computing (TSC), 2021. DOI Bibtex PDF
WC Magazine	[27] Ali Hussain Khan, Naveed UL Hassan, Chau Yuen, Jun Zhao, Dusit Niyato, Yan Zhang, H. Vincent Poor, "Blockchain and 6G: The Future of Secure and Ubiquitous Communication," in IEEE Wireless Communications, 2021. DOI Bibtex PDF Bibtex @ARTICLE{9508931, author={Khan, Ali Hussain and Hassan, Naveed UL and Yuen, Chau and Zhao, Jun and Niyato, Dusit and Zhang, Yan and Poor, H. Vincent}, journal={IEEE Wireless Communications}, title={Blockchain and 6G: The Future of Secure and Ubiquitous Communication}, year={2021}, volume={}, number={}, pages={1-8}, doi={10.1109/MWC.001.2100255}}
WC Magazine	[28]. Jianhang Tang, Jiangtian Nie, Jun Zhao, Yi Zhou, Zehui Xiong, and Mohsen Guizani. "Slicing-Based Software-Defined Mobile Edge Computing in the Air", IEEE Wireless Communications. DOI Bibtex PDF
IEEE Network	[29]. Helin Yang, Arokiaswami Alphones, Zehui Xiong, Dusit Niyato, Jun Zhao, and Kaishun Wu, “Artificial Intelligence-Enabled Intelligent 6G Networks”, IEEE Network, 2020. DOI Bibtex PDF
IEEE Network	[30]. Xin Liu, Kwok-Yan Lam, Feng Li, Jun Zhao, Li Wang, and Tariq S. Durrani. "Spectrum Sharing for 6G Integrated Satellite-Terrestrial Communication Networks Based on NOMA and CR." IEEE Network, volume 35, issue 4 (2021): 28-34. DOI Bibtex PDF
Neurocomputing	[31]. Donghaisheng Liu, Shoudong Han, Yang Chen, Chenfei Xia, and Jun Zhao. "Foreground-Guided Textural-Focused Person Re-Identification." Neurocomputing, 2022. DOI Bibtex PDF Bibtex @article{LIU2022235, title = {Foreground-guided textural-focused person re-identification}, journal = {Neurocomputing}, volume = {483}, pages = {235-248}, year = {2022}, issn = {0925-2312}, doi = {https://doi.org/10.1016/j.neucom.2022.02.014}, url = {https://www.sciencedirect.com/science/article/pii/S0925231222001564}, author = {Donghaisheng Liu and Shoudong Han and Yang Chen and Chenfei Xia and Jun Zhao}, keywords = {Person re-identification, Foreground guidance, Texture reconstruction}, abstract = {Person re-identification (Re-ID) is challenging in computer vision. It is crucial to strengthen discriminative features and suppress irrelevant ones for high-performance. Existing Re-ID approaches have won significant progress by attention mechanisms or introducing key-point/part priors. However, these methods suffer the high cost of building models. Besides, potential feature false alarm problems may happen due to background interference and inaccurate priors. Moreover, they usually ignore the degradation of texture clues which are identity sensitive. In this paper, we propose the Foreground-Guided Textural-Focused Network (FTN) to address these problems. Specifically, our FTN is an end-to-end Re-ID framework, which consists of a Semantic Encoder (S-Enc), a Compact Foreground Attention (CFA) module, and a Texture-Focused Decoder (TF-Dec). First, based on CFA and 2D Gaussian kernel, a coarse foreground-guided learning strategy is developed to suppress the feature false alarms at the source. Its core idea lies in constructing foreground guidance, which forces S-Enc to pay more attention to person instance-level features. Second, the TF-Dec is designed as a lightweight reconstruction task. It is trained via a novel gradient loss, and further forces S-Enc to maintain texture-wise details. Our method is computationally efficient as TF-Dec is abandoned in the inference phase. Extensive experiments are conducted on three large-scale Re-ID datasets Market1501, CUHK03, MSMT17, and two other occluded datasets. The results indicate that FTN achieves superior performance against the state-of-the-art methods, e.g., Rank-1 of 96.2% and 59.0% on Market1501 and Occluded-Duke respectively.} }
Neurocomputing	[32]. Teng Wang, Jun Zhao, Zhi Hu, Xinyu Yang, Xuebin Ren and Kwok-Yan Lam, “Local Differential Privacy for data collection and analysis”, Neurocomputing, 2021. DOI Bibtex PDF Bibtex @article{WANG2021114, title = {Local Differential Privacy for data collection and analysis}, journal = {Neurocomputing}, volume = {426}, pages = {114-133}, year = {2021}, issn = {0925-2312}, doi = {https://doi.org/10.1016/j.neucom.2020.09.073}, url = {https://www.sciencedirect.com/science/article/pii/S0925231220316064}, author = {Teng Wang and Jun Zhao and Zhi Hu and Xinyu Yang and Xuebin Ren and Kwok-Yan Lam}, keywords = {Data collection and analysis, -local differential privacy, Stochastic gradient descent, Data utility}, abstract = {Local Differential Privacy (LDP) can provide each user with strong privacy guarantees under untrusted data curators while ensuring accurate statistics derived from privatized data. Due to its powerfulness, LDP has been widely adopted to protect privacy in various tasks (e.g., heavy hitters discovery, probability estimation) and systems (e.g., Google Chrome, Apple iOS). In particular, (∊,δ)-LDP has been studied in related statistical tasks like private learning and hypothesis testing, but is mainly achieved by using Gaussian mechanism, leading to the limited data utility. In this paper, we investigate several novel mechanisms that achieve (∊,δ)-LDP with higher data utility in collecting and analyzing users’ data. Specifically, we first design two (∊,δ)-LDP algorithms for mean estimations on multi-dimensional numeric data, which can ensure higher accuracy than the optimal Gaussian mechanism. Then, we investigate different local protocols for frequency estimations on categorical attributes under (∊,δ)-LDP. Based on the proposed mechanisms, we further study on (∊,δ)-LDP-compliant stochastic gradient descent algorithms for machine learning models. Besides, the theoretical analysis of the error bound and the variance of the proposed algorithms are also presented in the paper. We have conducted extensive experiments on both real-world and synthetic datasets and demonstrated the high data utility of our proposed algorithms in the perspectives of simple data statistics tasks and complex machine learning tasks. The experimental results have shown that our proposed algorithms can effectively improve the data utility in different tasks while alleviating the privacy concerns of each individual.} }
Pattern Recognition	[33]. Ziqing Yang, Shoudong Han, and Jun Zhao, “Poisson kernel: Avoiding self-smoothing in graph convolutional networks”, Pattern Recognition, 2021. DOI Bibtex PDF Bibtex @article{YANG2022108443, title = {Poisson kernel: Avoiding self-smoothing in graph convolutional networks}, journal = {Pattern Recognition}, volume = {124}, pages = {108443}, year = {2022}, issn = {0031-3203}, doi = {https://doi.org/10.1016/j.patcog.2021.108443}, url = {https://www.sciencedirect.com/science/article/pii/S0031320321006191}, author = {Ziqing Yang and Shoudong Han and Jun Zhao}, keywords = {Graph convolutional kernel, Graph convolutional network, Graph neural network, Graph structure, Self-smoothing}, abstract = {Graph convolutional network is now an effective tool to deal with non-Euclidean data, such as social behavior analysis, molecular structure analysis, and skeleton-based action recognition. Graph convolutional kernel is one of the most significant factors in graph convolutional networks to extract nodes’ feature, and some variants of it have achieved highly satisfactory performance theoretically and experimentally. However, there was limited research about how exactly different graph structures influence the performance of these kernels. Some existing methods used an adaptive convolutional kernel to deal with a given graph structure, which still not explore the internal reasons. In this paper, we start from theoretical analysis of the spectral graph and study the properties of existing graph convolutional kernels, revealing the self-smoothing phenomenon and its effect in specific structured graphs. After that, we propose the Poisson kernel that can avoid self-smoothing without training any adaptive kernel. Experimental results demonstrate that our Poisson kernel not only works well on the benchmark datasets where state-of-the-art methods work fine, but also is evidently superior to them in synthetic datasets.} }
Pattern Recognition	[34]. Ge Song, Xiaoyang Tan, Jun Zhao, and Ming Yang. "Deep robust multilevel semantic hashing for multi-label cross-modal retrieval." Pattern Recognition 120 (2021): 108084. DOI Bibtex PDF Bibtex @article{SONG2021108084, title = {Deep robust multilevel semantic hashing for multi-label cross-modal retrieval}, journal = {Pattern Recognition}, volume = {120}, pages = {108084}, year = {2021}, issn = {0031-3203}, doi = {https://doi.org/10.1016/j.patcog.2021.108084}, url = {https://www.sciencedirect.com/science/article/pii/S0031320321002715}, author = {Ge Song and Xiaoyang Tan and Jun Zhao and Ming Yang}, keywords = {Hashing, Multi-label, Cross-modal retrieval, Deep learning}, abstract = {Hashing based cross-modal retrieval has recently made significant progress. But straightforward embedding data from different modalities involving rich semantics into a joint Hamming space will inevitably produce false codes due to the intrinsic modality discrepancy and noises. We present a novel deep Robust Multilevel Semantic Hashing (RMSH) for more accurate multi-label cross-modal retrieval. It seeks to preserve fine-grained similarity among data with rich semantics,i.e., multi-label, while explicitly require distances between dissimilar points to be larger than a specific value for strong robustness. For this, we give an effective bound of this value based on the information coding-theoretic analysis, and the above goals are embodied into a margin-adaptive triplet loss. Furthermore, we introduce pseudo-codes via fusing multiple hash codes to explore seldom-seen semantics, alleviating the sparsity problem of similarity information. Experiments on three benchmarks show the validity of the derived bounds, and our method achieves state-of-the-art performance.} }
IoTJ	[35]. Jianhang Tang, Jiangtian Nie, Zehui Xiong, Jun Zhao, Yang Zhang, Dusit Niyato, "Slicing-based Reliable Resource Orchestration for Secure Software Defined Edge-Cloud Computing Systems," in IEEE Internet of Things Journal, 2021. DOI Bibtex PDF Bibtex @ARTICLE{9521992, author={Tang, Jianhang and Nie, Jiangtian and Xiong, Zehui and Zhao, Jun and Zhang, Yang and Niyato, Dusit}, journal={IEEE Internet of Things Journal}, title={Slicing-based Reliable Resource Orchestration for Secure Software Defined Edge-Cloud Computing Systems}, year={2021}, volume={}, number={}, pages={1-1}, doi={10.1109/JIOT.2021.3107490}}
IoTJ	[36] Tao Bai, Jun Zhao, Jinlin Zhu, Shoudong Han, Jiefeng Chen, Bo Li, Alex Kot. Toward Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Method. In IEEE Internet of Things Journal (IoTJ), volume 9, issue 3, pages 1875-1884, Feb 2022. DOI Bibtex PDF Bibtex @ARTICLE{9463482, author={Tao Bai, Jun Zhao, Jinlin Zhu, Shoudong Han, Jiefeng Chen, Bo Li, Alex Kot}, journal={IEEE Internet of Things Journal}, title={Toward Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Method}, year={2021}, volume={}, number={}, pages={1-1}, doi={10.1109/JIOT.2021.3091683}}
IoTJ	[37]. Thippa Reddy Gadekallu, Quoc-Viet Pham, Dinh C. Nguyen, Praveen Kumar Reddy Maddikunta, N Deepa, Prabadevi B, Pubudu N. Pathirana, Jun Zhao, Won-Joo Hwang, "Blockchain for Edge of Things: Applications, Opportunities, and Challenges," in IEEE Internet of Things Journal (IoTJ), volume 9, issue 2, pages 964-988, January 2022. DOI Bibtex PDF Bibtex @ARTICLE{9569742, author={Gadekallu, Thippa Reddy and Pham, Quoc-Viet and Nguyen, Dinh C. and Maddikunta, Praveen Kumar Reddy and Deepa, N and B, Prabadevi and Pathirana, Pubudu N. and Zhao, Jun and Hwang, Won-Joo}, journal={IEEE Internet of Things Journal}, title={Blockchain for Edge of Things: Applications, Opportunities, and Challenges}, year={2021}, volume={}, number={}, pages={1-1}, doi={10.1109/JIOT.2021.3119639}}
IoTJ	[38]. Tao Bai, Jinqi Luo, Jun Zhao, "Inconspicuous Adversarial Patches for Fooling Image Recognition Systems on Mobile Devices," in IEEE Internet of Things Journal, 2021. DOI Bibtex PDF
IoTJ	[39]. Muhammad Baqer Mollah, Jun Zhao, Dusit Niyato, Yong Liang Guan, Chau Yuen, Sumei Sun, Kwok-Yan Lam, and Leong Hai Koh, "Blockchain for the Internet of Vehicles towards Intelligent Transportation Systems: A Survey," IEEE Internet of Things Journal, vol. 8, no. 6, pp.4157-4185, March 2021. DOI Bibtex PDF Bibtex @article{mollah2020blockchainiov, title={Blockchain for the Internet of Vehicles towards Intelligent Transportation Systems: A Survey}, author={Mollah, Muhammad Baqer and Zhao, Jun and Niyato, Dusit and Guan, Yong Liang and Yuen, Chau and Sun, Sumei and and Lam, Kwok-Yan and Koh, Leong Hai}, journal={IEEE Internet of Things Journal}, year={2021}, volume={8}, number={6}, pages={4157-4185} }
IoTJ	[40]. Wei Sun, Qingyang Song, Jun Zhao, Lei Guo, and Abbas Jamalipour, “Adaptive Resource Allocation in SWIPT-Enabled Cognitive IoT Networks", in IEEE Internet of Things Journal, 2021. DOI Bibtex PDF
IoTJ	[41]. Muhammad Baqer Mollah, Jun Zhao, Dusit Niyato, Yong Liang Guan, Chau Yuen, Sumei Sun, Kwok-Yan Lam, and Leong Hai Koh, "Blockchain for future smart grid: A comprehensive survey", IEEE Internet of Things Journal, vol. 8, no. 1, pp. 18-43, January 2021. DOI Bibtex PDF
IoTJ	[42]. Yang Zhao, Jun Zhao, Mengmeng Yang, Teng Wang, Ning Wang, Lingjuan Lyu, Dusit Niyato, and Kwok-Yan Lam, “Local Differential Privacy based Federated Learning for Internet of Things”, IEEE Internet of Things Journal (IoTJ), 2020. DOI Bibtex PDF Bibtex @ARTICLE{9253545, author={Zhao, Yang and Zhao, Jun and Yang, Mengmeng and Wang, Teng and Wang, Ning and Lyu, Lingjuan and Niyato, Dusit and Lam, Kwok-Yan}, journal={IEEE Internet of Things Journal}, title={Local Differential Privacy based Federated Learning for Internet of Things}, year={2021}, volume={8}, number={11}, pages={8836-8853}, doi={10.1109/JIOT.2020.3037194}}
IoTJ	[43]. Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, Dusit Niyato, Zengxiang Li, Lingjuan Lyu, and Yingbo Liu, “Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices”, IEEE Internet of Things Journal (IoTJ), 2020. DOI Bibtex PDF
IoTJ	[44]. Yang Zhao, Jun Zhao, Jiawen Kang, Zehang Zhang, Dusit Niyato, Shuyu Shi, “A Blockchain-Based Approach for Saving and Tracking Differential-Privacy Cost”, IEEE Internet of Things Journal (IoTJ), 2020. DOI Bibtex PDF Bibtex @ARTICLE{9351532, author={Zhao, Yang and Zhao, Jun and Kang, Jiawen and Zhang, Zehang and Niyato, Dusit and Shi, Shuyu and Lam, Kwok-Yan}, journal={IEEE Internet of Things Journal}, title={A Blockchain-Based Approach for Saving and Tracking Differential-Privacy Cost}, year={2021}, volume={8}, number={11}, pages={8865-8882}, doi={10.1109/JIOT.2021.3058209}}
IoTJ	[45]. Quoc-Viet Pham, Thien Huynh-The, Mamoun Alazab, Jun Zhao, Won-Joo Hwang, "Sum-Rate Maximization for UAV-assisted Visible Light Communications using NOMA: Swarm Intelligence meets Machine Learning," IEEE Internet of Things Journal (IoTJ), 2020. DOI Bibtex PDF
IoTJ	[46]. Xuebin Ren, Chia-Mu Yu, Wei Yu, Xinyu Yang, Jun Zhao, Shusen Yang. "DPCrowd: Privacy-preserving and Communication-efficient Decentralized Statistical Estimation for Real-time Crowd-sourced Data", IEEE Internet of Things Journal (IoTJ), 2020. DOI Bibtex PDF
IoTJ	[47]. Huimei Han, Lushun Fang, Weidang Lu, Wenchao Zhai, Ying Li, Jun Zhao, “A GCICA Grant-Free Random Access Scheme for M2M Communications in Crowded Massive MIMO Systems", in IEEE Internet of Things Journal (IoTJ), 2021. DOI Bibtex PDF Bibtex @ARTICLE{9530567, author={Han, Huimei and Fang, Lushun and Lu, Weidang and Zhai, Wenchao and Li, Ying and Zhao, Jun}, journal={IEEE Internet of Things Journal}, title={A GCICA Grant-Free Random Access Scheme for M2M Communications in Crowded Massive MIMO Systems}, year={2021}, volume={}, number={}, pages={1-1}, doi={10.1109/JIOT.2021.3110793}}
TGCN	[48]. Xiaolun Jia, Xiangyun Zhou, Dusit Niyato, Jun Zhao. "Intelligent Reflecting Surface-Assisted Bistatic Backscatter Networks: Joint Beamforming and Reflection Design", IEEE Transactions on Green Communications and Networking, 2021. DOI Bibtex PDF
T-IT	[49]. Jun Zhao. “Probabilistic key predistribution in mobile networks resilient to node-capture attacks,” IEEE Transactions on Information Theory (T-IT), volume 63, issue 10, pages 6714–6734 (21 pages), October 2017. DOI Bibtex PDF
T-IT	[50]. Jun Zhao, Osman Yağan, and Virgil Gligor. "k-Connectivity in random key graphs with unreliable links," IEEE Transactions on Information Theory (T-IT), volume 61, issue 7, pages 3810-3836 (27 pages), July 2015. DOI Bibtex PDF
T-IT	[51]. Faruk Yavuz, Jun Zhao, Osman Yagan, and Virgil Gligor. “Toward k-connectivity of the random graph induced by a pairwise key predistribution scheme with unreliable links,” IEEE Transactions on Information Theory (T-IT), volume 61, issue 11, pages 6251–6271 (21 pages), November 2015. DOI Bibtex PDF Bibtex @article{article, author = {Yavuz, Faruk and Zhao, Jun and Yagan, Osman and Gligor, Virgil}, year = {2014}, month = {04}, pages = {}, title = {Toward $k$ -Connectivity of the Random Graph Induced by a Pairwise Key Predistribution Scheme With Unreliable Links}, volume = {61}, journal = {IEEE Transactions on Information Theory (T-IT)}, doi = {10.1109/TIT.2015.2471295} }
T-IT	[52]. Faruk Yavuz, Jun Zhao, Osman Yagan, and Virgil Gligor. “k-Connectivity in random K-out graphs intersecting Erdos–Rényi graphs,” IEEE Transactions on Information Theory (T-IT), volume 63, issue 3, pages 1677–1692, March 2017. DOI Bibtex PDF
TON	[53]. Jun Zhao. “Topological properties of secure wireless sensor networks under the q-composite key predistribution scheme with unreliable links,” IEEE/ACM Transactions on Networking (TON), volume 25, issue 3, pages 1789–1802 (14 pages), June 2017. DOI Bibtex PDF
TAC	[54]. Jun Zhao, Osman Yağan, and Virgil Gligor. "On connectivity and robustness in random intersection graphs," IEEE Transactions on Automatic Control (TAC), volume 62, issue 5, pages 2121-2136 (16 pages), May 2017. DOI Bibtex PDF
TSIPN	[55]. Jun Zhao. “Analyzing resilience of interest-based social networks against node and link failures,” IEEE Transactions on Signal and Information Processing over Networks (TSIPN), volume 3, issue 2, pages 252–267 (16 pages), June 2017. DOI Bibtex PDF
TSIPN	[56]. Jun Zhao and Junshan Zhang. "Preserving privacy enables 'co-existence equilibrium' of competitive diffusion in social networks," IEEE Transactions on Signal and Information Processing over Networks (TSIPN), volume 3, issue 2, pages 282-297 (16 pages), June 2017. DOI Bibtex PDF
TCNS	[57]. Jun Zhao. “Analyzing connectivity of heterogeneous secure sensor networks,” IEEE Transactions on Control of Network Systems (TCNS), volume 5, issue 1, pages 618–628 (11 pages), March 2018. DOI Bibtex PDF
TCNS	[58]. Jun Zhao. “Transitional behavior of q-composite random key graphs with applications to networked control,” IEEE Transactions on Control of Network Systems (TCNS), volume 5, issue 4, pages 1741 - 1751 (11 pages), Dec 2018. DOI Bibtex PDF
ICDCS	[59]. Xinyu Zhou, Jun Zhao, Huimei Han and Claude Guet. "Joint Optimization of Energy Consumption and Completion Time in Federated Learning," IEEE International Conference on Distributed Computing Systems (ICDCS), July 2022. DOI Bibtex PDF
ICDCS	[60]. Jun Zhao, Jing Tang, Li Zengxiang, Huaxiong Wang, Kwok-Yan Lam, and Kaiping Xue. "An Analysis of Blockchain Consistency in Asynchronous Networks: Deriving a Neat Bound," IEEE International Conference on Distributed Computing Systems (ICDCS), July 2020. DOI Bibtex PDF Bibtex @inproceedings{ZhaoICDCS, author = {Zhao, Jun and Tang, Jing and Li, Zengxiang and Wang, Huaxiong and Lam, Kwok-Yan and Xue, Kaiping}, year = {2020}, month = {09}, pages = {}, Jounal = {IEEE International Conference on Distributed Computing Systems (ICDCS)}, title = {An Analysis of Blockchain Consistency in Asynchronous Networks: Deriving a Neat Bound} }
ICDCS	[61]. Jun Zhao. “Secure connectivity of wireless sensor networks under q-composite key predistribution with on/off channels,” Proceedings of IEEE 37th Annual International Conference on Distributed Computing Systems (ICDCS), pages 889–899 (11 pages), Atlanta, Georgia, US, June 2017. DOI Bibtex PDF
ICDCS	[62]. Xiao Wang, Xinbing Wang, and Jun Zhao, "Impact of Mobility and Heterogeneity on Coverage and Energy Consumption in Wireless Sensor Networks," in IEEE International Conference on Distributed Computing Systems (ICDCS), 2011. DOI Bibtex PDF
INFOCOM	[63]. Zhiying Xu, Shuyu Shi, Alex X. Liu, Jun Zhao, Lin Chen, "An Adaptive and Fast Convergent Approach to Differentially Private Deep Learning", IEEE Conference on Computer Communications (INFOCOM), 2020. DOI Bibtex PDF Bibtex @inproceedings{inproceedings, author = {Xu, Zhiying and Shi, Shuyu and Liu, Alex and Zhao, Jun and Chen, Lin}, Jounal = {IEEE Conference on Computer Communications (INFOCOM) 2020}, year = {2020}, month = {04}, pages = {}, title = {An Adaptive and Fast Convergent Approach to Differentially Private Deep Learning}, doi = {10.1109/INFOCOM41043.2020.9155359} }
INFOCOM	[64]. Shuyu Shi, Yaxiong Xie, Mo Li, Alex X. Liu, and Jun Zhao, "Synthesizing Wider Wifi Bandwidth For Respiration Rate Monitoring In Dynamic Environments", IEEE Conference on Computer Communications (IEEE INFOCOM), pp. 181-189, 2019. DOI Bibtex PDF Bibtex @INPROCEEDINGS{shi8737553, author={S. {Shi} and Y. {Xie} and M. {Li} and A. X. {Liu} and J. {Zhao}}, booktitle={IEEE INFOCOM 2019 - IEEE Conference on Computer Communications}, title={Synthesizing Wider WiFi Bandwidth for Respiration Rate Monitoring in Dynamic Environments}, year={2019}, volume={}, number={}, pages={181-189}, doi={10.1109/INFOCOM.2019.8737553}}
INFOCOM	[65]. Chenhui Hu, Xinbing Wang, Ding Nie, and Jun Zhao, "Multicast Scaling Laws with Hierarchical Cooperation," in IEEE Conference on Computer Communications (INFOCOM), 2010. DOI Bibtex PDF
IJCAI	[66]. Tao Bai, Jinqi Luo, Jun Zhao, Bihan Wen, Qian Wang. Recent Advances in Adversarial Training for Adversarial Robustness. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI), pages 4312-4321, 2021. DOI Bibtex PDF Bibtex @inproceedings{ijcai2021-591, title = {Recent Advances in Adversarial Training for Adversarial Robustness}, author = {Bai, Tao and Luo, Jinqi and Zhao, Jun and Wen, Bihan and Wang, Qian}, booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, {IJCAI-21}}, publisher = {International Joint Conferences on Artificial Intelligence Organization}, editor = {Zhi-Hua Zhou}, pages = {4312--4321}, year = {2021}, month = {8}, note = {Survey Track} doi = {10.24963/ijcai.2021/591}, url = {https://doi.org/10.24963/ijcai.2021/591}, }
SIGMOD	[67]. Renchi Yang, Xiaokui Xiao, Zhewei Wei, Sourav Bhowmick, Jun Zhao, and Rong-Hua Li. "Efficient Algorithms for Local Clustering via Heat Kernels". ACM SIGMOD International Conference on Management of Data (SIGMOD), full paper, pp. 1339–1356, 2019. Acceptance rate: 18%. DOI Bibtex PDF Bibtex @inproceedings{ry10, author = {Yang, Renchi and Xiao, Xiaokui and Wei, Zhewei and Bhowmick, Sourav S. and Zhao, Jun and Li, Rong-Hua}, title = {Efficient Estimation of Heat Kernel PageRank for Local Clustering}, year = {2019}, isbn = {9781450356435}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, doi = {10.1145/3299869.3319886}, pages = {1339–1356}, numpages = {18}, keywords = {local clustering, heat kernel pagerank}, location = {Amsterdam, Netherlands}, series = {SIGMOD '19} }
ICDE	[68]. Ning Wang, Xiaokui Xiao, Yin Yang, Jun Zhao, Siu Cheung Hui, Hyejin Shin, Junbum Shin, and Ge Yu. “Collecting and analyzing multidimensional data with local differential privacy,” IEEE International Conference on Data Engineering (ICDE), full paper, pp. 638-649, 2019. DOI Bibtex PDF Bibtex @inproceedings{wang2019collecting, title={Collecting and analyzing multidimensional data with local differential privacy}, author={Wang, Ning and Xiao, Xiaokui and Yang, Yin and Zhao, Jun and Hui, Siu Cheung and Shin, Hyejin and Shin, Junbum and Yu, Ge}, booktitle={2019 IEEE 35th International Conference on Data Engineering (ICDE)}, pages={638--649}, year={2019}, organization={IEEE} }
CIKM	[69]. Teng Wang, Jun Zhao, Han Yu, Jinyan Liu, Xinyu Yang, Xuebin Ren, and Shuyu Shi, “Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas,” in ACM International Conference on Information and Knowledge Management (CIKM), full paper, 2019. DOI Bibtex PDF
WirelessComLetter	[70]. Yue Xiu, Jun Zhao, Ertugrul Basar, Marco Di Renzo, Guan Gui, Zhongpei Zhang, “Uplink Achievable Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave Systems with Resolution-Adaptive ADCs”, IEEE Wireless Communications Letters, 2021. DOI Bibtex PDF Bibtex @ARTICLE{9390351, author={Xiu, Yue and Zhao, Jun and Basar, Ertugrul and Renzo, Marco Di and Sun, Wei and Gui, Guan and Wei, Ning}, journal={IEEE Wireless Communications Letters}, title={Uplink Achievable Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave Systems With Resolution-Adaptive ADCs}, year={2021}, volume={10}, number={8}, pages={1608-1612}, doi={10.1109/LWC.2021.3069644}}
ComLetter	[71]. Yue Xiu, Jun Zhao, Wei Sun and Zhongpei Zhang, "Secrecy Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave System with Low-resolution DACs", IEEE Communications Letters, 2021. DOI Bibtex PDF Bibtex @ARTICLE{9348933, author={Xiu, Yue and Zhao, Jun and Sun, Wei and Zhang, Zhongpei}, journal={IEEE Communications Letters}, title={Secrecy Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave System With Low-Resolution DACs}, year={2021}, volume={25}, number={7}, pages={2166-2170}, doi={10.1109/LCOMM.2021.3057379}}
ComLetter	[72]. Yue Xiu, Jun Zhao, Chau Yuen, Zhongpei Zhang, Guan Gui, “Secure Beamforming for Multiple Intelligent Reflecting Surfaces Aided mmWave Systems”, IEEE Communications Letters, 2020. DOI Bibtex PDF
Applied Energy	[73]. Chaoyu Dong, Xiangke Li, Wentao Jiang, Yunfei Mu, Jun Zhao, Hongjie Jia, “Cyber-physical Modelling Operator and Multimodal Vibration in the Integrated Local Vehicle-Grid Electrical System", Elsevier Applied Energy, 2021. DOI Bibtex PDF Bibtex @article{DONG2021116432, title = {Cyber-physical modelling operator and multimodal vibration in the integrated local vehicle-grid electrical system}, journal = {Applied Energy}, volume = {286}, pages = {116432}, year = {2021}, issn = {0306-2619}, doi = {https://doi.org/10.1016/j.apenergy.2021.116432}, url = {https://www.sciencedirect.com/science/article/pii/S0306261921000015}, author = {Chaoyu Dong and Xiangke Li and Wentao Jiang and Yunfei Mu and Jun Zhao and Hongjie Jia}, keywords = {Cyber-physical operator, Electrical grid, Electric vehicle, Multimodal vibration, Participation factor}, abstract = {The interaction of the individual user and the electrical are deeply strengthened with wireless communication and internet of things development. In the region of urban, the growing penetration of electric vehicles and the corresponding fast charging techniques not only emerge an effective interface for the virtual power plant, but also emerge the vulnerable points for the cyber attack. Facing the complexity of the physical vehicle-grid system and the uncertainty of the communication network, the unified modelling with a cyber-physical operator is established in this work. By the implicit Runge-Kutta method, the cyber-physical dynamics of the vehicle-grid electrical system is narrowed in the finite-dimensional space, which mimics the latency and denial-of-service attacks with a discretized matrix. After that, the techniques of space transformation and the subspace mapping are introduced to further approximate the hybrid system by a reduced cyber-physical operator. The resulting operator simultaneously preserves the system structure and reduces the dimensions, which provides the opportunity for the extraction of dynamic indexes and the participation factor. The proposed cyber-physical operator has experimentally verified in the case studies as well as the disclosure of the multimodal vibration. It is found that the interface of open cyber network and the lower-inertia batteries inside the electric vehicles might become the potential attacking points for the integrated local energy system. By the malicious cyber attacks, the stability feature of the cyber-physical system could be reshaped to a multimodal shape, which injects inharmonious energy from the cyber network into the physical system.} }
Best Paper Award	[74]. Zehui Xiong, Jun Zhao, Jiawen Kang, Dusit Niyato, Ruilong Deng, Shengli Xie, “Towards the Future Data Market: Reward Optimization in Mobile Data Subsidization”, 3rd EAI International Conference on B5G/6G for Future Wireless Networks (6GN), 2020. Best Paper Award. DOI Bibtex PDF
Best Paper Award	[75]. Helin Yang, Kwok-Yan Lam, Jiangtian Nie, Jun Zhao, Sahil Garg, Liang Xiao, Zehui Xiong, Mohsen Guizani. 3D Beamforming Based on Deep Learning for Secure Communication in 5G and Beyond Wireless Networks. 2021 IEEE Globecom Workshops (GC Wkshps). Best Paper Award. DOI Bibtex PDF Bibtex @INPROCEEDINGS{9681960, author={Yang, Helin and Lam, Kwok-Yan and Nie, Jiangtian and Zhao, Jun and Garg, Sahil and Xiao, Liang and Xiong, Zehui and Guizani, Mohsen}, booktitle={2021 IEEE Globecom Workshops (GC Wkshps)}, title={3D Beamforming Based on Deep Learning for Secure Communication in 5G and Beyond Wireless Networks}, year={2021}, volume={}, number={}, pages={1-6}, doi={10.1109/GCWkshps52748.2021.9681960}}
ComSoc Best Readings	[76]. Huimei Han, Jun Zhao, Dusit Niyato, Marco Di Renzo, Quoc-Viet Pham, "Intelligent Reflecting Surface Aided Network: Power Control for Physical-Layer Broadcasting", IEEE International Conference on Communications (IEEE ICC), Dublin, Ireland, 2020, pp. 1-7. Listed as ComSoc Best Readings at https://www.comsoc.org/publications/best-readings/reconfigurable-intelligent-surfaces DOI Bibtex PDF Bibtex @INPROCEEDINGS{hh9148827, author={H. {Han} and J. {Zhao} and D. {Niyato} and M. D. {Renzo} and Q. {Pham}}, booktitle={ICC 2020 - 2020 IEEE International Conference on Communications (ICC)}, title={ Surface Aided Network: Power Control for Physical-Layer Broadcasting}, year={2020}, volume={}, number={}, pages={1-7}, doi={10.1109/ICC40277.2020.9148827}}
ComSoc Best Readings	[77]. Yang Liu, Jun Zhao, Zehui Xiong, Dusit Niyato, Chau Yuen, Cunhua Pan, Binbin Huang, “Intelligent Reflecting Surface Meets Mobile Edge Computing: Enhancing Wireless Communications for Computation Offloading”, 2021. Listed as ComSoc Best Readings at https://www.comsoc.org/publications/best-readings/reconfigurable-intelligent-surfaces DOI Bibtex PDF

[78]. Xiaokui Xiao, Ergute Bao, Jun Zhao, Dongping Zhang, Bolin Ding. "Synthetic Data Generation with Differential Privacy via Bayesian Networks," Journal of Privacy and Confidentiality.
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[79]. Huimei Han, Jun Zhao, Xinyu Zhou, "A Random Access Scheme for Federated Learning over Massive MIMO Systems," submitted to IEEE Transactions on Communications.
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[80]. Tinghao Zhang, Kwok-Yan Lam, Jun Zhao, Feng Li, Huimei Han, Norziana Jamil. Enhancing Federated Learning with spectrum allocation optimization and device selection. submitted to IEEE/ACM Transactions on Networking, 2022.
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[81]. Wanting Lyu, Yue Xiu, Jun Zhao, Zhongpei Zhang, "Optimizing the Age of Information in RIS-aided SWIPT Networks," submitted to IEEE Transactions on Vehicular Technology.
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[82]. Tao Bai, Jun Zhao, Bihan Wen, "Guided Adversarial Contrastive Distillation for Robust Students," submitted to IEEE Transactions on Information Forensics and Security (TIFS).
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[83]. Tao Bai, Jun Zhao, Lanqing Guo, and Bihan Wen, "Adversarial Purification through Representation Disentanglement".
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[84].Lingjuan Lyu, Han Yu, Xingjun Ma, Lichao Sun, Jun Zhao, Qiang Yang, Philip S. Yu, “Privacy and Robustness in Federated Learning: Attacks and Defenses”
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[85]. Mengmeng Yang, Lingjuan Lyu; Jun Zhao, Tianqing Zhu, Kwok-Yan Lam, “Local Differential Privacy and Its Applications: A Comprehensive Survey”.
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[86]. Huimei Han, Wenchao Zhai, Zhefu Wu, Ying Li, Jun Zhao, Mingda Chen, “A Novel Random Access Scheme for M2M Communication in Crowded Asynchronous Massive MIMO Systems”, IET Communications, 2021.
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[87]. Wenchao Zhai, Yishan Wu, Jun Zhao and Huimei Han, "6G Downlink Transmission via Rate Splitting Space Division Multiple Access Based on Grouped Code Index Modulation," 2021 IEEE International Conference on Communications Workshops (ICC Workshops), pages. 1-6, 2021.
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[88]. Yang Liu, Jun Zhao, Ming Li, Qingqing Wu. Intelligent Reflecting Surface Aided MISO Uplink Wireless Network: Feasibility and Power Control. IEEE ICC 2021 Workshop on Reconfigurable Intelligent Surfaces for future wireless communications, 2021.
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[89]. Xiaoyu Wang, Tao Bai and Jun Zhao. "A Data-Free Approach for Targeted Universal Adversarial Perturbation." In International Conference on Science of Cyber Security, pages. 126-138, Springer, Cham, 2021.
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[90]. Helin Yang, Jun Zhao, Jiangtian Nie, Neeraj Kumar, Kwok-Yan Lam, Zehui Xiong. "UAV-Assisted 5G/6G Networks: Joint Scheduling and Resource Allocation Based on Asynchronous Reinforcement Learning." In IEEE INFOCOM 2021-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pages 1-6. IEEE, 2021.
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[91]. Helin Yang, Jun Zhao, Kwok-Yan Lam, Zehui Xiong, Liang Xiao and Qingqing Wu. COVID-19 diagnosis system based on federated transfer learning. submitted to Scientific Reports, August 2021.
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[92]. Bai, Tao, Jun Zhao, Jinlin Zhu, Shoudong Han, Jiefeng Chen, Bo Li, and Alex Kot. "AI-GAN: Attack-inspired generation of adversarial examples." In 2021 IEEE International Conference on Image Processing (ICIP), pages 2543-2547. IEEE, 2021.
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[93]. Huimei Han, Wenchao Zhai, Weidang Lu, Xinyuan Yang, Liping Qian, Jun Zhao, "LSTM-Aided Hybrid Random Access Scheme for 6G Machine Type Communication Networks. submitted to IEEE Systems Journal, Sept 2021.
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[94]. Helin Yang, Jun Zhao, Kwok-Yan Lam, Sahil Garg, Qingqing Wu and Zehui Xiong. “Deep Reinforcement Learning Based Resource Allocation for Heterogeneous Networks”, The 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2021), October 11-13, 2021, Bologna, Italy.
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[95].Xu, Zhiying; Shi, Shuyu; Liu, Alex; Zhao, Jun; Chen, Lin; Lam, Kwok-Yan; Gu, Yu. ADADP: Improving Differentially Private DeepLearning via Adaptive Learning Rate and Noise. submitted to IEEE Transactions on Mobile Computing, October 2021.
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[96]. Weiheng Jiang, Bolin Chen, Sahil Garg, Jiangtian Nie, Jun Zhao and Prof. Zehui Xiong. Joint Transmit Precoding and Reflect Beamforming Design for IRS-Assisted MIMO-OFDM Secure Communications. GLOBECOM 2021 - 2021 IEEE Global Communications Conference
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[97]. Yanze Zhu, Yang Liu, Jun Zhao, Ming Li and Qingqing Wu. Joint Time Allocation and Beamforming Design for IRS-Aided Coexistent Cellular and Sensor Networks. GLOBECOM 2021 - 2021 IEEE Global Communications Conference
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[98]. Jiale Guo, Ziyao Liu, Kwok-Yan Lam, Jun Zhao and Yiqiang Chen. “Privacy-Enhanced Federated Learning with Weighted Aggregation”, The 7th International Symposium on Security and Privacy in Social Networks and Big Data (SocialSec 2021), November 19-21, 2021, Fuzhou, China.
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[99]. Xiong Xi, Yuanyuan Li, Fei Xiong, Jun Zhao, Shaojie Qiao, Shenggen Ju, “Dynamic Discovery of Favorite Locations in Spatio-temporal Social Networks”, Information Processing & Management, 2020.
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[100]. Jinqi Luo, Tao Bai and Jun Zhao, “Generating Adversarial yet Inconspicuous Patches with a Single Image, In Proceedings of the AAAI Conference on Artificial Intelligence 2021 Student Abstract and Poster Program, 2021.”
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[101]. Helin Yang, Jun Zhao , Kwok-Yan Lam, Zehui Xiong, Liang Xiao, Qingqing Wu, “Privacy-preserving federated transfer learning system for COVID-19 diagnosis"
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[102]. Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Qingqing Wu, Massimo Tornatore, Stefano Secci. “Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning”, IEEE Global Communications Conference (GLOBECOM) 2020.
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[103]. Yulan Gao, Chao Yong, Zehui Xiong, Dusit Niyato, Yue Xiao, Jun Zhao. "A Stackelberg Game Approach to Resource Allocation for IRS-aided Communications", IEEE Global Communications Conference (GLOBECOM) 2020.
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[104]. Helin Yang, Yang Zhao, Zehui Xiong, Jun Zhao, Dusit Niyato, Kwok-Yan Lam, and Qingqing Wu, "Deep reinforcement learning based intelligent reflecting surface for secure wireless communications", in IEEE Global Communications Conference (GLOBECOM), 2020.
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[105]. Yulan Gao, Chao Yong, Zehui Xiong, Dusit Niyato, Yue Xiao, Jun Zhao. "Resource Allocation for Intelligent Reflecting Surface Aided Cooperative Communications", IEEE Global Communications Conference (GLOBECOM) 2020.
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[106]. Xiaolun Jia, Jun Zhao, Xiangyun Zhou, and Dusit Niyato. "Intelligent Reflecting Surface-Aided Backscatter Communications", IEEE Global Communications Conference (GLOBECOM) 2020.
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[107]. Yue Xiu, Yang Zhao, Yang Liu, Jun Zhao, Osman Yagan, and Ning Wei, “IRS-Assisted Millimeter Wave Communications: Joint Power Allocation and Beamforming Design”, 2021.
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[108]. Yulan Gao, Chao Yong, Zehui Xiong, Dusit Niyato, Yue Xiao, Jun Zhao, “Reconfigurable Intelligent Surface for MISO Systems with Proportional Rate Constraints”, IEEE International Conference on Communications (IEEE ICC), 2020.
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[109]. Yunchuan Liu, Amir Ghasemkhani, Lei Yang, Jun Zhao, Junshan Zhang, and Vijay Vittal, “Seasonal Self-evolving Neural Networks Based Short-term Wind Farm Generation Forecast”, IEEE SmartGridComm 2020.
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[110]. Hans Albert Lianto, Yang Zhao, Jun Zhao, “Attacks to Federated Learning: Responsive Web User Interface to Recover Training Data from User Gradients”, Poster in ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS) 2020.
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[111]. Leong Mei Han, Yang Zhao, Jun Zhao, “Blockchain-Based Differential Privacy Cost Management System”, Poster in ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS) 2020.
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[112]. Lingjuan Lyu, Han Yu, Jun Zhao and Qiang Yang, “Threats to Federated Learning”, Federated Learning: Privacy and Incentive (FLPI) 2020.
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[113]. Yang Zhao, Jun Zhao, Wenchao Zhai, Sumei Sun, Dusit Niyato and Kwok-Yan Lam, “A Survey of 6G Wireless Communications: Emerging Technologies”, Future of Information and Communication Conference (FICC) 2020.
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[114]. Lingjuan Lyu, Yee Wei Law, Kee Siong Ng, Shibei Xue, Jun Zhao, Mengmeng Yang and Lei Liu. "Towards Distributed Privacy-Preserving Prediction", IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2020.
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[115]. "Intelligent Reflecting Surface Aided Max-min Fair Multigroup Multicast MISO System", 2020.
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[116]. Wei Sun, Qingyang Song, Lei Guo and Jun Zhao, "Secrecy Rate Maximization for Intelligent Reflecting Surface Aided SWIPT Systems", IEEE CIC ICCC 2020.
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[117]. Huaqiang Xu, Guodong Zhang, Jun Zhao and Quoc-Viet Pham, "Intelligent reflecting surface aided wireless networks: Harris Hawks optimization for beamforming design". IEEE 6th International Conference on Computer and Communications (ICCC), 2020.
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[118]. Lin Sun, Xiaojun Ye, Jun Zhao, Chenhui Lu, Mengmeng Yang, “BiSample: Bidirectional Sampling for Handling Missing Data with Local Differential Privacy” 25th International Conference on Database Systems for Advanced Applications (DASFAA), 2020.
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[119]. Helin Yang, Jun Zhao , Kwok-Yan Lam, Zehui Xiong, Liang Xiao, Qingqing Wu, “Privacy-preserving federated transfer learning system for COVID-19 diagnosis"
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[120]. Huy T. Nguyen, Nguyen Cong Luong, Jun Zhao, Chau Yuen, and Dusit Niyato, "Resource allocation in mobility-aware federated learning networks: A deep reinforcement learning approach, "IEEE World Forum on The Internet of Things (WF-IoT), New Orleans, Louisiana, USA, 5-9 April 2020.
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[121]. Binbin Huang, Zhongjin Li, Peng Tang, Shangguang Wang, Jun Zhao, Haiyang Hu, Wanqing Li, and Victor Chang, “Security modeling and efficient computation offloading for service workflow in mobile edge computing”, Future Generation Computer Systems (FGCS) 97 (2019): 755-774.
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[122]. Teng Wang, Xinyu Yang, Xuebin Ren, Jun Zhao, and Kwok-Yan Lam. "Adaptive Differentially Private Data Stream Publishing in Spatio-temporal Monitoring of IoT." IEEE 38th International Performance Computing and Communications Conference (IPCCC), pp. 1-8. IEEE, 2019.
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[123]. Zehui Xiong, Jun Zhao, Dusit Niyato, Ping Wang, Yang Zhang, "Design of contract-based sponsorship scheme in Stackelberg game for sponsored content market," IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 9-13 December 2019.
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[124]. Yutao Jiao, Ping Wang, Dusit Niyato, Jun Zhao, Bin Li, and Dong In Kim, "Task allocation and mobile base station deployment in wireless powered spatial crowdsourcing," in Proceedings of IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Beijing, China, 21-24 October 2019.
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[125]. Linshan Jiang, Xin Lou, Rui Tan, and Jun Zhao, “Differentially private collaborative learning for the IoT edge,” International Workshop on Crowd Intelligence for Smart Cities: Technology and Applications (CICS), held in conjunction with International Conference on Embedded Wireless Systems and Networks (EWSN), Beijing, China, February 2019.
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[126]. Jun Zhao, “Distributed Deep Learning under Differential Privacy with the Teacher-Student Paradigm” AAAI Workshops 2018: 404-408
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[127]. Jun Zhao. "Analyzing the robustness of deep learning against adversarial examples", IEEE 56th Allerton Conference on Communication, Control, and Computing (Allerton), 2018., pp. 1060-1064
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[128]. Wubing Chen, Xian Shi, Zhiying Xu, Shuyu Shi, Yang Zhao, and Jun Zhao "A Survey of Blockchain Applications in Different Domains", International Conference on Blockchain Technology and Application, 2018.
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[129]. Songjun Ma, Ge Chen, Luoyi Fu, Weijie Wu, Xiaohua Tian, Jun Zhao, and Xinbing Wang. “Seeking powerful information initial spreaders in online social networks: A dense group perspective,” Wireless Networks (Springer), 2018.
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[130]. Jun Zhao. “Adaptive statistical learning with Bayesian differential privacy,” ACM 16th Annual CCS Workshop on Privacy in the Electronic Society, held in conjunction with ACM SIGSAC 25th Annual Conference on Computer and Communications Security (CCS), Dallas, Texas, US, October 2017.
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[131]. Jun Zhao, Junshan Zhang, and Vincent Poor. "Dependent differential privacy for correlated data," Proceedings of IEEE 5th Annual Workshop on Trusted Communications with Physical Layer Security, held in conjunction with IEEE Annual Global Communications Conference (GLOBECOM), Singapore, December 2017.
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[132]. Jun Zhao. “Modeling interest-based social networks: Superimposing Erdos–Rényi graphs over random intersection graphs,” Proceedings of IEEE 42nd Annual International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 3704–3708, New Orleans, Louisiana, US, March 2017.
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[133]. Jun Zhao. “Relations among different privacy notions,” IEEE 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), UIUC, Illinois, US, October 2017.
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[134]. Jun Zhao. “Composition properties of Bayesian differential privacy,” IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, Quebec, Canada, October 2017.
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[135]. Jun Zhao. “Designing secure networks with q-composite key predistribution under different link constraints,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2017.
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[136]. Jun Zhao. “Topology control in secure wireless sensor networks,” in Cyber security for industrial control systems: From the viewpoint of close-loop, pages 183–224 (41 pages), CRC Press, Taylor & Francis Group, 2016 (Invited Book Chapter).
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[137]. Jun Zhao. “A comprehensive guideline for choosing parameters in the Eschenauer–Gligor key predistribution,” IEEE Allerton Conference on Communication, Control, and Computing (Allerton), 2016.
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[138]. Faruk Yavuz, Jun Zhao, Osman Yagan, and Virgil Gligor. “Designing secure and reliable wireless sensor networks under a pairwise key predistribution scheme,” IEEE International Conference on Communications (ICC), 2015.
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[139]. Jun Zhao, Osman Yagan, and Virgil Gligor. “Exact analysis of k-connectivity in secure sensor networks with unreliable links,” Proceedings of IEEE 13th Annual International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt), pages 191–198, Mumbai, India, May 2015.
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[140]. Jun Zhao. “On the resilience to node capture attacks of secure wireless sensor networks,” in IEEE Allerton Conference on Communication, Control, and Computing (Allerton), 2015.
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[141]. Jun Zhao, Osman Yagan, and Virgil Gligor. “On k-connectivity and minimum vertex degree in random s-intersection graphs,” in ACM-SIAM Meeting on Analytic Algorithmics & Combinatorics (ANALCO), 2015.
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[142]. Jun Zhao. “Sharp transitions in random key graphs,” in IEEE Allerton Conference on Communication, Control, and Computing (Allerton), 2015.
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[143]. Jun Zhao. “Threshold functions in random s-intersection graphs,” in IEEE Allerton Conference on Communication, Control, and Computing (Allerton), 2015.
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[144]. Jun Zhao. “The absence of isolated node in geometric random graphs,” in IEEE Allerton Conference on Communication, Control, and Computing (Allerton), 2015.
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[145]. Jun Zhao. “Critical behavior in heterogeneous random key graphs,” in IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015.
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[146]. Jun Zhao. “Parameter control in predistribution schemes of cryptographic keys,” in IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015.
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[147]. Jun Zhao, Osman Yagan, and Virgil Gligor. “On topological properties of wireless sensor networks under the q-composite key predistribution scheme with on/off channels,” Proceedings of IEEE Annual International Symposium on Information Theory (ISIT), pages 1131–1135, Honolulu, Hawaii, US, June 2014. The work was shortlisted for the best student paper award.
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[148]. Jun Zhao. “Minimum node degree and k-connectivity in wireless networks with unreliable links,” in IEEE International Symposium on Information Theory (ISIT), 2014.
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[149]. Jun Zhao, Osman Yagan, and Virgil Gligor. “On the strengths of connectivity and robustness in general random intersection graphs,” Proceedings of IEEE 53rd Annual Conference on Decision and Control (CDC), pages 3661–3668, Los Angeles, California, US, December 2014.
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[150]. Jun Zhao, Osman Yagan, and Virgil Gligor, "Connectivity in Secure Wireless Sensor Networks under Transmission Constraints," in IEEE Allerton Conference on Communication, Control, and Computing (Allerton), 2014.
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[151]. Faruk Yavuz, Jun Zhao, Osman Yagan, and Virgil Gligor, "On Secure and Reliable Communications in Wireless Sensor Networks: Towards k-connectivity under a Random Pairwise Key Predistribution Scheme," in IEEE International Symposium on Information Theory (ISIT), 2014.
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[152]. Jun Zhao, Osman Yagan, and Virgil Gligor, "Secure k-connectivity in Wireless Sensor Networks under an on/off Channel Model," in IEEE International Symposium on Information Theory (ISIT), 2013.
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[153]. Jun Zhao, Virgil Gligor, Adrian Perrig, and James Newsome, "ReDABLS: Revisiting Device Attestation with Bounded Leakage of Secrets,"Lecture Notes in Computer Science (for Cambridge Workshop on Security Protocols), 2013.
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[154]. Virgil Gligor, Adrian Perrig, and Jun Zhao, "Brief Encounters with a Random Key Graph," Lecture Notes in Computer Science, 2013.
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[155]. Wei Sun, Yue Xiu, Jun Zhao, Qingyang Song, Haotong Cao, and Gagangeet Singh Aujla. "Secure Transmission in Intelligent Reflecting Surface-Aided NOMA-SWIPT Systems"
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[156]. Huimei Han, Jun Zhao, Wenchao Zhai, Zehui Xiong and Weidang Lu, "Smart City Enabled by 5G/6G Networks: An Intelligent Hybrid Random Access Scheme".
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[157]. Feng Li, Kwok-Yan Lam, Min Jia, Jun Zhao, Xiuhua Li, Li Wang, “Blockchain-Based Approach for Securing Spectrum Trading in Multibeam Satellite Systems”
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[158]. “Real-time indoor localization and communications using an integrated visible light communication and localization system”
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[159]. Zhiying Xu, Shuyu Shi, Alex X. Liu, Jun Zhao, Lin Chen, and Yusheng Ji. "Cloud/Edge Co-Design for Device-Customized, Accurate and Private Deep Learning Service".
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[160]. Shan Te Chao, Yang Zhao and Jun Zhao, "Reviewing Blockchain Scalability Challenge with a Discussion of Off-Chain Approaches".
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[161]. Quoc-Viet Pham, Nhu-Ngoc Dao, Thien Huynh-The, Jun Zhao, Won-Joo Hwang, “Clustering and Power Allocation for UAV-assisted NOMA-VLC Systems: A Swarm Intelligence Approach”
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[162]. Jun Zhao. "Optimizations with Intelligent Reflecting Surfaces (IRSs) in 6G Wireless Networks: Power Control, Quality of Service, Max-Min Fair Beamforming for Unicast, Broadcast, and Multicast with Multi-antenna Mobile Users and Multiple IRSs", 2019.
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[163]. Jun Zhao, “A Survey of Intelligent Reflecting Surfaces (IRSs): Towards 6G Wireless Communication Networks”, 2019
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[164]. Deyou Zhang, Ang Li, Jun Zhao, Branka Vucetic, and Yonghui Li. "Channel Tracking for Intelligent Reflecting Surface Enabled Networks." 2020.
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[165]. Jun Zhao, Junshan Zhang, and Vincent Poor. “Dependent differential privacy,” submitted to IEEE Transactions on Information Theory (T-IT).
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[166]. Jun Zhao, Osman Yağan, and Virgil Gligor. “On asymptotically exact probability of k-connectivity in random key graphs intersecting Erdos–Rényi graphs.” submitted to IEEE Transactions on Information Theory (T-IT).
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[167]. Helin Yang, Chen Chen, Arokiaswami Alphones, Jun Zhao, and Kwok Yan Lam. "Experimental Demonstration of An Indoor Integrated Visible Light Communication and Positioning System". Under Review in IEEE Transactions on Industrial Electronics.
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[168]. Helin Yang, Chen Chen, Arokiaswami Alphones, Jun Zhao, and Kwok Yan Lam. "Heterogeneous RF/VLC Wireless Networks Based on Intelligent Resource Management for 5G and Beyond". Under Review in IEEE Transactions on Wireless Communications (TWC)
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[169]. Lin Sun, Jun Zhao, and Xiaojun Ye, “Distributed Clustering in the Anonymized Space with Local Differential Privacy”, 2019
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[170]. Lin Sun, Jun Zhao, Xiaojun Ye, Shuo Feng, Teng Wang, and Tao Bai, “Conditional Analysis for Key-Value Data with Local Differential Privacy”, 2019.
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[171]. Jun Zhao, Teng Wang, Tao Bai, Kwok-Yan Lam, Zhiying Xu, Shuyu Shi, Xuebin Ren, Xinyu Yang, Yang Liu, and Han Yu, “Reviewing and Improving the Gaussian Mechanism for Differential Privacy”, 2019
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[172]. Baichuan Huang, Jun Zhao, and Jingbin Liu, “A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks”, 2020
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[173]. Zhiying Xu, Shuyu Shi, Alex X. Liu, Jun Zhao, Lin Chen, "An Adaptive and Fast Convergent Approach to Deep Learning under Differential Privacy", submitted to IEEE Transactions on Dependable and Secure Computing (TDSC).
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[174]. Huimei Han, Jun Zhao, Wenchao Zhai, Zehui Xiong and Weidang Lu, "Smart City Enabled by 5G/6G Networks: An Intelligent Hybrid Random Access Scheme".
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[175]. Hongliang Zhang, Shoudong Han, Xiaofeng Pan, Jun Zhao, “ANL: Anti-Noise Learning for Cross-Domain Person Re-Identification", submitted to IEEE Transactions on Circuits and Systems for Video Technology, 2020
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[176] Kentaroh Toyoda, Jun Zhao, Allan N. Zhang and P. Takis Mathiopoulos, “Blockchain-enabled Federated Learning with Mechanism Design”, IEEE Access, 2020.
[177]. Tao Bai, Jinqi Luo, Jun Zhao, “Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks", 2020.
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[177]. Yang Zhao, Wenchao Zhai, Jun Zhao, Tinghao Zhang, Sumei Sun, Dusit Niyato and Kwok-Yan Lam, "A Comprehensive Survey of 6G Wireless Communications".
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