A Deep Learning Method for Joint Compression and Unsupervised Denoising of CSI Feedback

In this work, we propose a deep learning approach for jointly compressing and denoising the CSI feedback in massive MIMO systems. We consider a practical scenario where only noisy CSI is available for training and inference. To jointly denoise and compress the CSI feedback for improved reconstruction quality without having access to true CSI, we propose a novel generic loss function based on the Stein's unbiased risk estimator (SURE) for unsupervised denoising, and the evidence lower bound (ELBO) for CSI compression. This is in contrast to most existing supervised denoising methods that either require knowledge of the true CSI or are limited to high SNR regimes. Empirically, we show that the proposed approach improves the reconstruction quality of the state-of-the-art method. Moreover, the proposed approach is independent of the choice of the encoder-decoder architecture and can be easily extended to the existing volume of work on this topic.