Bayesian Hierarchical Sparse Autoencoder for Massive MIMO CSI Feedback

Efficient channel state information (CSI) compression and feedback from user equipment to the base station (BS) are crucial for achieving the promised capacity gains in massive multiple-input multiple-output (MIMO) systems. Deep autoencoder (AE)-based schemes have been proposed to improve the efficiency of CSI compression and feedback. However, existing AE-based schemes suffer from critical issues in both CSI dimensionality reduction and latent feature quantization. In this paper, we propose a novel hierarchical sparse AE for efficient CSI compression and feedback for the 5G-NR fixed-length CSI feedback mechanism. Our approach employs a two-tier AE structure to jointly compress the sparse CSI latent feature and its side information. Additionally, we utilize a model-assisted Bayesian Rate-Distortion approach to train the weights of the AE. Specifically, the training loss function is formulated based on the variational Bayesian inference framework given a parametric Bernoulli Laplace Mixture prior model and a sparsity-inducing likelihood model. Furthermore, we propose a model-assisted adaptive coding algorithm to quantize the latent feature under the fixed codeword bit length constraint. Our experimental results demonstrate that the proposed solution outperforms existing AE-based schemes under various feedback budgets.