Distributed Learning-Based Channel Estimation and Feedback for RIS-Aided Wireless Communications

To reduce the required pilot and feedback overhead in reconfigurable intelligent surface (RIS)-aided wireless communication systems under frequency division duplex (FDD) mode, a deep distributed learning-based joint channel estimation and feedback framework is proposed. Specifically, in the channel estimation stage, an RIS channel estimation network (RIS-CENet) is designed to reconstruct the channel from limited pilots by using the dual-channel attention mechanism. In the channel feedback stage, the RIS channel feedback network (RIS-CFNet), based on a multi-head attention mechanism, is designed to capture the global features of high-dimensional channel matrices, thereby enabling their effective compression. Furthermore, an efficient distributed learning framework is proposed to achieve channel estimation and feedback tasks in complex and rapidly changing wireless channel environments. Numerical results show that the performance of the proposed distributed learning architecture is comparable to that of the centralized learning architecture. Distributed scheme I has better feedback accuracy, while distributed scheme II can effectively reduce the complexity of the system.