报告人：郑淦

时间：2021年12月16日16：00

地点：腾讯会议直播（ID：711701511)

嘉宾简介

郑途博士现为英国拉夫堡大学教授，现为IEEE Fellow并担任IEEE Wireless Communications Letters和IEEE Communications Letters编委，郑涂的研究主要关注无线通信网络的资源优化以及人T智能在通信系统中的应用，普6次获得IEEE最佳论文奖，包括2013年IEEE Signa Processing Letters（该奖项引入后首次唯一获奖论文），2015年GLOBECOM（国际通信领域两大顶级会议之一，通信信号处理Symposium唯一获奖论文）和2018年IEEE Technical Committee on Green Communications&Computina最佳论文（唯一获奖论文），他在权威国际期刊和丰要国际会议分别发表学术论文87篇和50篇。学术引用总数超过8743次，单篇最高引用802次，h-index为45（Google Scholar）.

讲座内容

Beamforming has been a key multi-antenna technique to improve the spectrum efficiency of 5G communications systems, but its optimisation is a difficult problem and therefore has not achieved its full potential. Traditional model-based numerical solutions are too complex and not effective in addressing model uncertainties. More recent data-based deep learning solutions face practica challenges such as sample efficiency, generalisation and poor performance in dynamic environments. In this talk. I will introduce our recent development in leveraging model-driven deep learning algorithms for the optimisation of beamforming. We will demonstrate that by properly incorporating available model knowledge in the neural network design, significant advantages can be achieved over state of the art for beyond 5G networks in terms of enhanced spectral efficiency, reduced complexity and channel estimation overhead, better generalisation and scalability.