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Robust Wireless Channel Estimation Using Machine Learning by Professor John Thompson

Time:October 20, 2023          Browse:

On October 13, 2023, the 56th Zhi·Xin forum, jointly organized by College of Electronic and Information Engineering, Tongji University and Shanghai Research Institute for Intelligent Autonomous Systems,was held in Room 305 at the Zhixin Buidling. Prof. John Thompson from the Institute for Imaging, Data & Communications of the University of Edinburgh was invited to give a talk on "Robust Wireless Channel Estimation Using Machine Learning ", which was moderated by Prof. Chao Wang.

Firstly, Prof. John Thompson introduced the connection between the University of Edinburgh and Tongji University, the University of Edinburgh, and the Institute for Imaging, Data & Communications. Then he described the current status of Wireless 2030 and its key applications, energy efficiency, network evolution, spectrum issues, and machine learning techniques. After that, Prof. John Thompson presented two applications on which optimization and machine learning methods have been applied. The first is the optimization of beam training in an mm-wave wireless communication system, including system model, channel model, beam training method, and experimental results; the second is the channel estimation in future wireless networks, including ReEsNet networks, the proposed new method Channelformer, and experimental performance. At last, Prof. John Thompson analyzed and introduced some future research topics in green communication, including the energy efficiency of new devices, the energy efficiency of open RAN networks, using of full Pareto boundaries, and machine-learning -based conflict cancellation.

After the talk, Prof. John Thompson exchanged views with teachers and students on issues related to wireless channel estimation, and encouraged students to broaden their horizons and explore, discover and solve new scientific problems based on his own experience. This talk further broadened the horizons of our teachers and students and enhanced their knowledge and understanding of wireless channel estimation with machine learning technology.

Written by Zhang Hao

Photographed by Wang Yunjiao

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