no code implementations • 27 Nov 2023 • Xiangyi Li, Jiajia Guo, Chao-Kai Wen, Shi Jin
This paper proposes using neural architecture search (NAS) to automate the generation of scenario-customized CSI feedback NN architectures, thereby maximizing the potential of deep learning in exclusive environments.
no code implementations • 27 Apr 2022 • Xiangyi Li, Jiajia Guo, Chao-Kai Wen, Shi Jin, Shuangfeng Han, XiaoYun Wang
One efficient CSI feedback method is the Auto-Encoder (AE) structure based on deep learning, yet facing problems in actual deployments, such as selecting the deployment mode when deploying in a cell with multiple complex scenarios.
no code implementations • 4 Aug 2019 • Xiangyi Li, Huaming Wu
In multiple-input multiple-output (MIMO) systems, it is crucial of utilizing the available channel state information (CSI) at the transmitter for precoding to improve the performance of frequency division duplex (FDD) networks.