報告題目：A Deep Learning Approach to Robust Downlink Beamforming Optimization
報告人：鄭淦 教授
時 間：2024年4月10日上午10:00-12:00
地 點：科研樓208會議室
報告摘要：
Probabilistic robust downlink beamforming is importantto ensure mobile users’ quality of service when the transmitter (a base stationof access point) only has estimated channel state information and estimationerror statistics. This problem is challenging and until now only conservativesolutions exist. In this talk I will introduce a deep learning approach to dealwith the robust beamforming design by using model-based learning, dataaugmentation and graph neural networks. Simulation results show that ourproposed approach outperforms state of the art in both performance andcomplexity.
報告人簡介：
Professor Gan Zheng is a Fellow of IEEE and IET.His research interests focus on 6G and beyond wireless networks, with currentemphasis on machine learning and quantum computing for wireless communications,reconfigurable intelligent surfaces, and integrated satellite and terrestrialcommunications. He has published over 200 papers in international journals andconferences, which have received more than 12,000 citations. He received sixinternational best paper awards. He currently serves as an Associate Editor forIEEE Wireless Communications Letters and IEEE Transactions on Communications.
