Deep Learning Assisted Channel Estimation Refinement in Uplink OFDM Systems Under Time-Varying Channels
Rugui Yao, Qiannan Qin, Shengyao Wang, Nan Qi, Ye Fan, Xiaoya Zuo · 2021
In various practical orthogonal frequency-division multiplexing (OFDM) systems, the estimation accuracy at the receiver is challenging, and, specifically when operate over time-varying channels. This occurs mostly due to the presence of multipath Doppler shifts. Meanwhile, deep learning has quite recently demonstrated its superiority in extracting features information from big data. To this end, in this paper, a deep learning-assisted approach for channel estimation refinement is proposed in OFDM systems, under uplink time-varying channels. By exploitingfully-connected deep neural network (FC-DNN) properly, we successfully design a channel parameter refine network (CPR-Net) which combines deep learning with existing channel estimation algorithms. Simulation results demonstrate that, compared with conventional channel estimation algorithms, the proposed CPR-Net can significantly improve the estimation accuracy of channel parameters and provide more accurate and robust signal recovery performance.