A Recursive Discretization Compression Framework Combined with Selective State Space Model for Massive MIMO CSI Feedback

The quality of channel state information (CSI) feedback is critical for maximizing the spectral efficiency of massive multiple-input multiple-output systems. With multiple antenna arrays, the overhead of direct CSI feedback in frequency division duplex mode is usually large, and many CSI compression techniques have been proposed to alleviate this problem. Deep learning (DL) has achieved tremendous strides in CSI feedback. However, most current DL-based CSI compression methods utilize fully connected layers to achieve dimensionality reduction, which may be suboptimal for network optimization and result in noteworthy information loss and reduced CSI reconstruction accuracy. In this paper, we propose a novel recursive discretization compression framework with a selective state space model for CSI feedback, namely CsiMamba-RDC. The framework employs improved residual vector quantization to recursively refine CSI representation, reducing information loss and storage overhead. Additionally, we present an encoder-decoder model leveraging a selective state space model to extract diverse channel features.