Good News | SSE Professors Shuguang Cui and Chuan Huang Won IEEE GLOBECOM Best Paper Awards
Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks led by Professor Shuguang Cui from the School of Science and Engineering (SSE) and the Future Network of Intelligence Institute (FNii), as well as "Compressed Multiple Random Access with Energy Modulation" by the team of SSE Professor Chuan Huang, respectively received the Best Paper Awards at the IEEE Global Communications Conference (IEEE GLOBECOM) in Taipei, China on December 10, 2020.
About IEEE GLOBECOM
IEEE Global Communications Conference (GLOBECOM), held regularly in mid-December, is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications, including information theory, communications signal processing, wireless communications, wireless networks, optical networks, multimedia communications, and information security. Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks led by Professor Shuguang Cui from the School of Science and Engineering (SSE) and the Future Network of Intelligence Institute (FNii), as well as "Compressed Multiple Random Access with Energy Modulation" by the team of SSE Professor Chuan Huang, received the Best Paper Awards at this year’s IEEE GLOBECOM held in Taipei, China on December 10, 2020.
About the Best Papers
01. Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks
Authors：Ye Hu, Mingzhe Chen, Walid Saad, H. Vincent Poor and Shuguang Cui
Drones can potentially provide a cost-effective, flexible approach to boost the performance of wireless networks. However, effectively deploying drone base stations (DBSs) in a dynamic wireless environment is still challenging. In particular, designing a DBS trajectory that allows it to provide timely on-demand service is a major challenge, particularly when the ground users’ requests are highly unpredictable. In this case, the DBS's trajectory must be adaptively adjusted to satisfy the dynamic user access requests. To this end, a meta-learning algorithm is proposed in order to adapt the DBS's trajectory when it encounters novel environments, by tuning a reinforcement learning (RL) solution. The meta-learning algorithm provides a solution that adapts the DBS in novel environments quickly based on limited former experiences. The meta-tuned RL is shown to yield a faster convergence to the optimal coverage in unseen environments with a considerably low computation complexity, compared to the baseline policy gradient algorithm.
02. Compressed Multiple Random Access with Energy Modulation
Authors：Jianhao Huang, Han Zhang, Chuan Huang and Wei Zhang
Massive Machine-Type Communications is an important scenario and key technology for future wireless communications. In such a scenario, the base station needs to support massive device access; however, only a small number of devices are often active in the same time period and need to access the base station, i.e., the access of devices is sparse. The detection of active users and the recovery of user transmission information are hot issues in the research of next-generation networks. Unlike previous studies, this paper focuses on energy modulation for non-coherent communication in short-packet transmission and low SNR scenarios, which features higher system throughput than coherent transmission.
To address these issues, this paper designs an energy modulation plan for non-coherent multiple random access, aiming at the joint detection of user activity and transmission information using the statistical information of the channel. At the transmitter side, the information of active users is modulated on the energy of the user identity sequence. At the receiving end, by exploiting the sparse nature of user access, we have devised simple and efficient detection algorithms: first, the approximate message passing (AMP) algorithm is designed to estimate the sparse signal; then the estimated signal for each user is detected in energy. In this paper, we present the BER analysis based on this algorithm and design the near-optimal power constellation points to minimize the BER under this system. In addition, the trend of the performance of this system is analyzed in this paper, and the results show that the stable communication of this system can be ensured when the number of receiving antennas increases at a rate with the number of users (N).
About the Professors
Prof. CUI, Shuguang
Acting Dean, School of Science and Engineering (SSE)
Director, Future Network of Intelligence Institute ((FNii)
Professor Shuguang Cui is a Chief Scientist of the National Key Research and Development Program of China, a Thomson Reuters Highly Cited Researcher, a Changjiang Scholar (or Yangtze River Scholar), and an IEEE Fellow. Cui received his Ph.D. in Electrical Engineering from Stanford University, California, USA, in 2005. Afterwards, he has been working as assistant, associate, full, Chair Professors in Electrical and Computer Engineering at the Univ. of Arizona, Texas A&M University, and UC Davis, respectively. He is currently a Presidential Chair Professor at CUHK-Shenzhen, where he also holds the positions of Acting Dean of the School of Science and Engineering, Director of Future Network of Intelligence Institute, Director of The Chinese University of Hong Kong (Shenzhen)-JD AI Joint Laboratory, as well as Vice Director of Shenzhen Research Institute of Big Data.
His research interests include data-driven, AI-powered large-scale system control and resource management. Having published more than 280 papers in leading international journals and conferences, Cui was the recipient of the IEEE Signal Processing Society 2012 Best Paper Award. He has served as the general co-chair and TPC co-chairs for many IEEE conferences. He has also been serving as the area editor for IEEE Signal Processing Magazine, and associate editors for IEEE Transactions on Big Data, IEEE Transactions on Signal Processing, IEEE JSAC Series on Green Communications and Networking, and IEEE Transactions on Wireless Communications. He has been the elected member for IEEE Signal Processing Society SPCOM Technical Committee (2009~2014) and the elected Chair for IEEE ComSoc Wireless Technical Committee (2017~2018). He is a member of the Steering Committee for both IEEE Transactions on Big Data and IEEE Transactions on Cognitive Communications and Networking. He is also a member of the IEEE ComSoc Emerging Technology Committee. He was elected as an IEEE Fellow in 2013 and an IEEE ComSoc Distinguished Lecturer in 2014. In October 2017, based on his academic reputation in the field of IoT and data analytics, he was invited to Hangzhou by Alibaba Group as one of 13 scientists to provide strategic input to the establishment of the Alibaba DAMO Academy.
Professor Cui's achievements in 2020 include IEEE ICC Best Paper Award, IEEE ICIP Best Paper Finalist, IEEE GLOBECOM Best Paper Award, ICT 2020 Innovative Application Award, Chinagraph Award for Graphical Open Source Datasets, First Prize of Natural Science Award of Chinese Institute of Electronics, and First Prize of Technological Invention Award of China Institute of Communications.
Prof. HUANG, Chuan
Associate Professor, SSE
Prof. Huang received his Ph.D. degree in electrical engineering from Texas A&M University, College Station, TX, USA, in 2012. From 2012 to 2013, he was with Arizona State University, Tempe, AZ, USA, as a postdoctoral research fellow, and then promoted to an assistant research professor from 2013 to 2014. He was also a visiting scholar with the National University of Singapore and a research associate with Princeton University. He was selected into the National Overseas High-level Young Talents Program in 2016, and won the National Excellent Youth Fund in 2020.
His research interests are wireless communication and signal processing, artificial intelligence and optimization theory applications in wireless communication. He has published two books, one book chapter, and more than 80 papers, including more than 40 journal papers. He has undertaken projects such as the Natural Science Foundation and the Joint Fund of the Ministry of Education, with a total amount of over 10 million RMB, and relevant achievements have been applied in major national events. He has been serving as the editor for IEEE Transactions on Wireless Communications, IEEE Wireless Communications Letters, and IEEE Access. He is also a senior member of China Communications Association, secretary of member development sub-committee of IEEE Asia Pacific Communications Association, and a member of Student Competition Committee of IEEE Communications Association. He also served as the symposium chair of IEEE GLOBECOM 2019 and IEEE ICCC 2019/2020. He was invited to give academic reports at many international famous wireless communication conferences.