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题目：Energy Harvesting Communications for IoTWireless Sensors
Dr. Wei Zhang (F’15) received the Ph.D. degree in Electronic Engineering from the Chinese University of Hong Kong in 2005. He was a Research Fellow at Hong Kong University of Science & Technology in 2006-2007. He joined the UNSW in 2008 and is currently an Associate Professor at School of Electrical Engineering and Telecommunications. His research interests include cognitive radio, energy harvesting communications, heterogeneous networks and massive MIMO. He has received several awards for his work, including the IEEE Communications Society Asia-Pacific Outstanding Young Researcher Award in 2009, and three best paper awards from international conferences (Globecom2007, WCSP2011, GlobalSIP2014).Dr. Zhang is the Editor-in-Chief of IEEE Wireless Communications Letters. He is also the Editor for IEEE Transactions on Communications and for IEEE Transactions on Cognitive Communications and Networking. He is Vice Director of IEEE Communications Society Asia Pacific Board. He has served as Secretary for IEEE Wireless Communications Technical Committee. He is an elected member of SPCOM Technical Committee of IEEE Signal Processing Society. He also serves on the organizing committee of the IEEE ICASSP 2016, Shanghai and the IEEE GLOBECOM 2017, Singapore. He is a Fellow of the IEEE, Fellow of the IET, and IEEE Communications Society Distinguished Lecturer.
The explosive growth of Internet of Things (IoT) originates from the proliferation of smarter wireless sensor nodes in a wide range of remote monitoring applications. The IoT wireless sensor nodes need to be maintenance free and operate continuously, which will have to rely on energy harvesting for power. In this talk, we propose online discrete rate and power adaption policies for an energy harvesting communication of IoT wireless sensor nodes. The receiver periodically sends 1-bit feedback by comparing the channel power gain with a predetermined threshold. The transmitter correspondingly adjusts QAM level and transmission power based on the 1-bit feedback and the available battery energy. To determine the optimal channel threshold, adaptive M-QAM level and corresponding power allocation, we formulate a constrained optimization problem to maximize the throughput within a finite horizon. We further propose an efficient but suboptimal discrete rate and power policy. Our results show that the performance loss is negligible for the simple M-QAM adaption of the suboptimal policy that is attributed to the optimal choice of channel threshold.