Self-Supervised Learning for CSI Compression in FDD Massive MIMO Systems

In frequency division duplexing systems, the user equipment frequently reports the channel state information (CSI) to the base station. This information is crucial for link adaptation and beamforming tasks in massive MIMO systems. However, it consumes a large amount of bandwidth when the number of antennas and subcarriers increases. Currently, compression techniques have been used to reduce this bandwidth overhead. In this letter, we propose a novel learning-based technique for CSI compression. Our method exploits the bias/variance tradeoff for CSI compression based on a shallow neural network to approximate a sufficient statistic function for each sampled channel. Unlike prior work that adopts projection-based techniques for compression, our method interprets the model weights as the compressed representation. The experimental results confirm the advantages of the proposed method compared with other state-of-the-art models.