Environment-Aware Codebook Design for RIS-Assisted MU-MISO Communications: Implementation and Performance Analysis

TL;DR

This work tackles the CSI and RC design challenges in RIS-assisted MU-MISO downlink by introducing an environment-aware codebook protocol. It offline constructs a codebook of RIS configurations derived from statistical CSI using alternating optimization, and online selects the best codeword by estimating the composite end-to-end channels and applying ZF precoding. Theoretical analysis under perfect and imperfect CSI reveals how the proposed scheme scales with system dimensions and training overhead, and simulations confirm that it outperforms conventional environment-agnostic codebooks while offering a flexible overhead-performance trade-off. The approach enables reduced pilot overhead and computational complexity with robust performance gains, making it well-suited for future RIS-enabled wireless networks.

Abstract

Reconfigurable intelligent surface (RIS) provides a new electromagnetic response control solution, which can proactively reshape the characteristics of wireless channel environments. In RIS-assisted communication systems, the acquisition of channel state information (CSI) and the optimization of reflecting coefficients constitute major design challenges. To address these issues, codebook-based solutions have been developed recently, which, however, are mostly environment-agnostic. In this paper, a novel environment-aware codebook protocol is proposed, which can significantly reduce both pilot overhead and computational complexity, while maintaining expected communication performance. Specifically, first of all, a channel training framework is introduced to divide the training phase into several blocks. In each block, we directly estimate the composite end-to-end channel and focus only on the transmit beamforming. Second, we propose an environment-aware codebook generation scheme, which first generates a group of channels based on statistical CSI, and then obtains their corresponding RIS configuration by utilizing the alternating optimization (AO) method offline. In each online training block, the RIS is configured based on the corresponding codeword in the environment-aware codebook, and the optimal codeword resulting in the highest sum rate is adopted for assisting in the downlink data transmission. Third, we analyze the theoretical performance of the environment-aware codebook-based protocol taking into account the channel estimation errors. Finally, numerical simulations are provided to verify our theoretical analysis and the performance of the proposed scheme. In particular, the simulation results demonstrate that our protocol is more competitive than conventional environment-agnostic codebooks.

Figures (12)

• Figure 1: The performance versus overhead diagram, where the proposed scheme strikes a flexible tradeoff between the statistical CSI-based scheme and the optimal RIS configuration.
• Figure 2: An RIS-assisted multiuser communication system.
• Figure 3: The frame structure of the proposed protocol.
• Figure 4: Conventional channel estimation and passive beamforming scheme. (Red arrow: cascaded channel including the RC configuration)
• Figure 5: The proposed channel training-based protocol.