Shannon Meets Huffman – Huffman Coded Spatial Modulation
9:00, September 27, 2016
1012, New Science & Technology Building, North Campus
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 four best paper awards from international conferences (Globecom2007, WCSP2011, GlobalSIP2014, ICCC 2016). Dr. Zhang is the Editor-in-Chief of IEEE Wireless Communications Letters. He is also the Editor for IEEE TCom and for IEEE TCCN. Previously, he served as Editor for IEEE TWC in 2010-2015 and for IEEE JSAC in 2012-2014. Dr. Zhang 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 and Fellow of the IET. He is also an IEEE Communications Society Distinguished Lecturer.
Massive MIMO has been viewed as a promising PHY technical solution for 5G cellular networks. The main challenge of using large number of antennas is the high cost of radio frequency (RF) chains. Antenna switch enables multiple antennas to share a common RF chain. It also offers an additional spatial dimension, i.e., antenna index, which can be utilized for data transmission via both signal space and spatial dimension. In this talk, we present a Huffman coding based adaptive spatial modulation that generalizes both conventional spatial modulation and transmit antenna selection. Through Huffman coding, the transmit antennas can be activated with different probabilities. The optimal antenna activation probability is derived to maximize the Shannon capacity. When the input is discrete QAM signals, the optimal antenna activation probability is derived through minimizing symbol error rate. Numerical results show that the proposed Huffman coded spatial modulation offers considerable performance gains over conventional spatial modulation or transmit antenna selection.
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