Variational Bayesian Autoencoder for Channel Compression and Feedback in Massive MIMO Systems

In this paper, we propose a Variational Bayesian Autoencoder (VBA)-based channel state information (CSI) compression and feedback scheme for massive multiple-input multiple-output (MIMO) systems. The proposed scheme incorporates the model-assisted knowledge of low-dimensional feedback features and the sparsity of channel to achieve enhanced compression efficiency. We also design a CsiVBA architecture that outputs distributions of the feedback features and the channel at the encoder and decoder, respectively, which facilitates a Bayesian training formulation exploiting the underlying channel sparsity. In addition, we also propose a low-complexity training scheme for new networks of different bit rates, significantly reducing the retraining cost for new compression requirements. Simulation results show that the proposed scheme achieves better rate-distortion trade-offs than the state-of-the-art solutions.