Abstract: Accurate channel state information (CSI) is crucial for the 6G wireless communication systems to accommodate the growing demands of mobile broadband services. In massive multiple-input multiple-output (MIMO) systems, traditional CSI feedback approaches face challenges such as performance degradation due to the feedback delay and channel aging caused by user mobility. To address these issues, we propose a novel spatio-temporal predictive network (STPNet) that jointly integrates CSI feedback and prediction modules. STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI, which captures both the local and the global spatio-temporal features. In addition, the signal-to-noise ratio (SNR) adaptive module is designed to adapt flexibly to diverse feedback channel conditions. Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.

Keywords: massive MIMO; deep learning; CSI prediction; CSI feedback