Deep Learning Aided Signal Detection in OFDM Systems with Time-Varying Channels
Rugui Yao, Shengyao Wang, Xiaoya Zuo, Juan Xu, Nan Qi · 2019
In this paper, we propose a deep learning aided approach for signal detection in orthogonal frequency-division multiplexing (OFDM) systems with time-varying channels. The method simplifies the architecture of OFDM systems by treating OFDM receivers as a black box. We utilize fully-connected deep neural network (FC-DNN) properly and successfully simulate an end-to-end time-varying OFDM system. Compared with two conventional algorithms well-designed to deal with OFDM systems in time-varying environment, the proposed method does not need to estimate channel parameters to detect signals. Simulation results also demonstrate that the trained DNN model has the ability to remember the characteristics of wireless time-varying channels and provide more accurate and robust signal recovery performance.