CPMamba: Selective State Space Models for MIMO Channel Prediction in High-Mobility Environments

Abstract

Channel prediction is a key technology for improving the performance of various functions such as precoding, adaptive modulation, and resource allocation in MIMO-OFDM systems. Especially in high-mobility scenarios with fast time-varying channels, it is crucial for resisting channel aging and ensuring communication quality. However, existing methods suffer from high complexity and the inability to accurately model the temporal variations of channels. To address this issue, this paper proposes CPMamba -- an efficient channel prediction framework based on the selective state space model. The proposed CPMamba architecture extracts features from historical channel state information (CSI) using a specifically designed feature extraction and embedding network and employs stacked residual Mamba modules for temporal modeling. By leveraging an input-dependent selective mechanism to dynamically adjust state transitions, it can effectively capture the long-range dependencies between the CSIs while maintaining a linear computational complexity. Simulation results under the 3GPP standard channel model demonstrate that CPMamba achieves state-of-the-art prediction accuracy across all scenarios, along with superior generalization and robustness. Compared to existing baseline models, CPMamba reduces the number of parameters by approximately 50 percent while achieving comparable or better performance, thereby significantly lowering the barrier for practical deployment.