Deep Learning-Based Bitstream Error Correction for CSI Feedback

Deep learning (DL)-based channel state information (CSI) feedback algorithms for massive multiple-input multiple-output (MIMO) can provide high beamforming accuracy to improve the throughput. However, bitstream errors in the feedback process can significantly affect the performance of CSI reconstruction. In this letter, we focus on building high reconstruction accuracy CSI feedback algorithm in the presence of bitstream errors. Specifically, we firstly introduce a DL-based architecture named ATNet, which can improve at least 2.96dB Normalized Mean Square Error (NMSE) compared with the existing algorithm. Then, we propose an error correction block called ECBlock and a two-step training strategy. Compared with traditional methods, the proposed scheme can reduce the influence of quantization and bitstream errors more effectively and improve the reconstruction accuracy.