Deep Learning Assisted Channel Estimation Refinement in Uplink OFDM Systems Under Time-Varying Channels

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 multi-path 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 exploiting fully-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.