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Prof. Ekram Hossain

University of Manitoba, Canada.


He is a Member (Class of 2016) of the College of the Royal Society of Canada. Also, he is a Fellow of the Canadian Academy of Engineering. Dr. Hossain’s current research interests include design, analysis, and optimization beyond 5G/6G cellular wireless networks. He was elevated to an IEEE Fellow “for contributions to spectrum management and resource allocation in cognitive and cellular radio networks”. His research works have received 24,000+ citations (in Google Scholar, with h-index = 80). He received the 2017 IEEE ComSoc TCGCC (Technical Committee on Green Communications & Computing) Distinguished Technical Achievement Recognition Award “for outstanding technical leadership and achievement in green wireless communications and networking”. Dr. Hossain has won several research awards including the “2017 IEEE Communications Society Best Survey Paper Award and the 2011 IEEE Communications Society Fred Ellersick Prize Paper Award. He was listed as a Clarivate Analytics Highly Cited Researcher in Computer Science in 2017 and  2018. Currently he serves as the Editor-in-Chief of IEEE Press and an Editor for the IEEE Transactions on Mobile Computing. Previously, he served as the Editor-in-Chief for the IEEE Communications Surveys and Tutorials (2012-2016). He is a Distinguished Lecturer of the IEEE Communications Society and the IEEE Vehicular Technology Society. Also, he is an elected member of the Board of Governors of the IEEE Communications Society for the term 2018-2020.


Optimal resource allocation is a fundamental challenge for dense and heterogeneous wireless networks with massive wireless connections. Because of the non-convex nature of the optimization problem, it is computationally demanding to obtain the optimal resource allocation. Machine learning, especially Deep learning (DL), is a powerful tool where a  multi-layer neural network can be trained  to model a resource management algorithm  using network data. Therefore, resource allocation decisions can be obtained without intensive online computations which would be required otherwise for the solution of resource allocation problems.  Recently, deep reinforcement learning (DRL) has emerged as a promising technique in solving non-convex optimization problems. Unlike deep learning (DL), DRL does not require any optimal/near-optimal training dataset which is either unavailable or computationally expensive in generating synthetic data. In this talk, I shall present a novel centralized DRL-based downlink power allocation scheme for a multi-cell system intending to maximize the total network throughput. Specifically, I shall discuss a deep Q-learning (DQL) approach to achieve near-optimal power allocation policy. I shall present some simulation results to compare the proposed DRL-based power allocation scheme with the conventional schemes in a multi-cell scenario.

Prof. Jinhong Yuan

School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, Australia


Jinhong Yuan (M’02–SM’11–F’16) received the B.E. and Ph.D. degrees in electronics engineering from the Beijing Institute of Technology, Beijing, China, in 1991 and 1997, respectively. From 1997 to 1999, he was a Research Fellow with the School of Electrical Engineering, University of Sydney, Sydney, Australia. In 2000, he joined the School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, Australia, where he is currently a Professor and Head of Telecommunication Group with the School. He has published two books, five book chapters, over 300 papers in telecommunications journals and conference proceedings, and 50 industrial reports. He is a co-inventor of one patent on MIMO systems and two patents on low-density-parity-check codes. He has co-authored four Best Paper Awards and one Best Poster Award, including the Best Paper Award from the IEEE International Conference on Communications, Kansas City, USA, in 2018, the Best Paper Award from IEEE Wireless Communications and Networking Conference, Cancun, Mexico, in 2011, and the Best Paper Award from the IEEE International Symposium on Wireless Communications Systems, Trondheim, Norway, in 2007. He is an IEEE Fellow and currently serving as an Associate Editor for the IEEE Transactions on Wireless Communications. He served as the IEEE NSW Chapter Chair of Joint Communications/Signal Processions/Ocean Engineering Chapter during 2011-2014 and served as an Associate Editor for the IEEE Transactions on Communications during 2012-2017. His current research interests include error control coding and information theory, communication theory, and wireless communications.


In this work, we propose the user activity identification/channel estimation algorithm and design the efficient random access scheme for massive machine-type communications.

In the first part, we propose a transmission control scheme and design an approximate message passing (AMP) algorithm for the joint user identification and channel estimation (JUICE) in massive machine-type communications. By employing a step transmission control function for the proposed scheme, we derive the channel distribution experienced by the receiver to describe the effect of the transmission control on the design of algorithm. Based on that, we design an AMP algorithm by proposing a minimum mean squared error (MMSE) denoiser, to jointly identify the user activity and estimate their channels. We further derive the false alarm and miss detection probabilities to characterize the user identification performance of the proposed scheme. Furthermore, we optimize the transmission control function to maximize throughput. We then propose a deep learning aided list AMP algorithm to further improve the user identification performance. A neural network is employed to identify a suspicious device which is most likely to be falsely alarmed during the first round of the AMP algorithm. We propose to enforce the suspicious device to be inactive in every iteration of the AMP algorithm in the second round. The proposed scheme can effectively combat the interference caused by the suspicious device and thus improve the user identification performance.

In the second part, we investigate the design and analysis of coded slotted ALOHA (CSA) schemes for massive machine-type communications in the presence of channel erasure. We design the code probability distributions for CSA schemes with repetition codes and maximum distance separable codes to maximize the expected traffic load, under both packet erasure channels and slot erasure channels. By optimizing the convergence of the derived EXIT functions, the code probability distributions to achieve the maximum expected traffic load are obtained. Then, we derive the asymptotic throughput of CSA schemes over erasure channels.




Prof. Guoqiang Mao

Director, Center for Real-time Information Networks, University of Technology Sydney


Guoqiang Mao received PhD in telecommunications engineering in 2002 from Edith Cowan University, Australia. He was a Faculty member at the School of Electrical and Information Engineering, the University of Sydney between 2002 and 2014. He joined the University of Technology Sydney in February 2014 as Professor of Wireless Networking and Director of Center for Real-time Information Networks. He has published three books and over 200 papers in international conferences and journals, including over 100 papers in IEEE journals, that have been cited over 7,000 times.  He is an editor of the IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Wireless Communications, IEEE Transactions on Vehicular Technology and received “Top Editor” award for outstanding contributions to the IEEE Transactions on Vehicular Technology in 2011, 2014 and 2015. He is a co-chair of IEEE Intelligent Transport Systems Society Technical Committee on Communication Networks. He has served as a chair, co-chair and TPC member in a number of international conferences, and has received best paper awards from several leading international conferences. His research interest includes intelligent transportation systems, vehicular networks, Internet of Things, next generation mobile communication systems, and wireless localization techniques. He is a Fellow of IEEE and IET.


Intelligent transportation system (ITS) is an important development that applies advanced sensing, communication, big data analysis and control technologies to ground transportation in order to improve safety, mobility and efficiency. This talk will begin with a brief introduction to our work in vehicular networks, which started more than ten years ago. As we delve deeper into vehicular networks and interact more frequently with transportation stakeholders, we realize that ITS is a truly cross-disciplinary area, in order for vehicular networks to achieve its desired impact, we need to think beyond the traditional communication domain, and start to ponder the deeper-level questions of what fundamental changes can be brought by advanced sensing and communication techniques to transportation and how the applications of advanced sensing and communication techniques can help solve crucial transportation problems. To this end, we will introduce our more recent work of developing advanced IoT technology to transform our roads into smart roads, which in the shorter term, make our roads safer and more efficient while providing the fine-grained real-time traffic information for traffic management; in the longer term, provide the much-needed road infrastructure support for the future booming CAV revolution.


Prof. Byonghyo Shim

Dept. of Electrical and Computer Engineering, Seoul National University, Director of Information System Laboratory


Byonghyo Shim received the B.S. and M.S. degree in Control and Instrumentation Engineering (currently Electrical Eng.) from Seoul National University (SNU), Seoul, Korea, in 1995 and 1997, respectively, and the M.S. degree in Mathematics and the Ph.D. degree in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (UIUC), Urbana, in 2004 and 2005, respectively. From 1997 and 2000, he was with the Department of Electronics Engineering at the Korean Air Force Academy as an Officer (First Lieutenant) and an Academic Full-time instructor. He also had a short time research position in the DSP group of LG Electronics and DSP R&D Center, Texas Instruments Incorporated, Dallas, TX, in 1997 and 2004, respectively. From 2005 to 2007, he was with the Qualcomm Inc., San Diego, CA as a Senior/Staff Engineer working on CDMA systems. From 2007 to 2014, he was with the School of Information and Communication, Korea University, Seoul, Korea, as an assistance and associate professor. Since September 2014, he has been with the Dept. of Electrical and Computer Engineering, Seoul National University, where he is currently a Professor. His research interests include signal processing for wireless communications, statistical signal processing, estimation and detection, compressed sensing and matrix completion, and information theory. Dr. Shim was the recipient of the M. E. Van Valkenburg Research Award from the ECE Department of the University of Illinois (2005), Hadong Young Engineer Award from IEIE (2010), and Irwin Jacobs Award from Qualcomm and KICS (2016). He is a senior member of IEEE, a technical committee member of Signal Processing for Communications and Networking (SPCOM), an associate editor of IEEE Transactions on Signal Processing (TSP), IEEE Transactions on Communications (TCOM), IEEE Wireless Communications Letters (WCL), Journal of Communications and Networks (JCN), and a guest editor of IEEE Journal of Selected Areas in Communications (location awareness for radios and networks).

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