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ECE Special Seminar: Namyoon Lee (Intel) – “Advanced Interference Management Techniques with Limited Channel State Information”

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Abstract:

Interference creates a fundamental barrier in attempting to improve throughput in wireless networks, especially when multiple concurrent transmissions share the wireless medium. In recent years, new paradigms for interference management have emerged to tackle interference in a general class of wireless networks: interference shaping and interference exploitation. Both approaches offer better performance in interference-limited communication regimes than traditionally thought possible. This talk provides a high-level overview of several different interference shaping and exploitation techniques for single-hop, multi-hop, and multi-way interference networks. Graphical illustrations that explain the intuition behind each strategy are provided. The talk concludes with a discussion of some relevant research directions that make interference management algorithms more practical in the future.

 

Speaker Bio:

Namyoon Lee received his Ph. D. in the Department of Electrical and Computer Engineering at The University of Texas at Austin in 2014. He also received his M.S. degree in Electrical Engineering from KAIST, Daejeon in 2008 and B.E. degree from Korea University, Seoul, Korea in 2006. From February 2008 to June 2011, he was with Samsung Advanced Institute of Technology (SAIT) in Korea, where he designed next generation wireless communication systems and involved standardization activities of the 3GPP LTE-A. He was also with Nokia Research Center at Berkeley (NRC) as a Senior Research Engineer  from December 2014 to May 2015, where he participated in the design of future WLAN systems (e.g., IEEE 802.11ax and 802.11ay). He is now with Wireless Communications Research (WRC) at Intel Labs, Santa Clara, CA. His primal research interest is to develop and analyze future wireless communication systems using tools including multi-antenna network information theory, stochastic geometry, and machine learning algorithms.