Deep Autoencoder-based Massive MIMO CSI Feedback with Quantization and Entropy Coding

Techniques which leverage channel state information (CSI) at a transmitter to adapt wireless signals to changing propagation conditions have been shown to improve the reliability of modern multiple input multiple output (MIMO) communication systems. To reduce overhead, previous works have proposed to compress CSI matrices using a trained deep autoencoder (AE) at the receiver before feeding it back to the transmitter, and recent work has proposed to quantize and perform entropy coding on the compressed CSI to further reduce communication complexity. While these methods are effective, they either do not incorporate quantization and lossless coding into their end-to-end optimization, or do not achieve performance comparable to methods that do not use quantization and entropy coding. In this work, we propose a new AE-based feedback method which uses an entropy bottleneck layer to both quantize and losslessly code the compressed CSI. This bottleneck layer allows us to jointly optimize bit-rate and distortion to achieve a highly-compressed CSI representation which preserves important channel information. Our method achieves better reconstruction quality than existing autoencoder-based CSI feedback methods for a wide range of bit-rates on simulated data, in both indoor and outdoor wireless settings.