Low-Overhead dynamic codebook design for RIS-Aided multiuser MISO systems

Reconfigurable intelligent surfaces (RIS) have emerged as a transformative technology in wireless communications, enabling dynamic channel manipulation through economical hardware configurations to enhance network performance. However, optimizing their reflection coefficients still relies heavily on accurate channel state information (CSI), leading to high pilot overhead and increased system complexity. To overcome these limitations, we propose a probability update (PU)-based dynamic codebook framework for RIS-aided multiuser multiple-input single-output (MU-MISO) communication systems. By iteratively updating the probability distribution of RIS phase shifts according to the performance of previously selected reflection coefficients, the PU codebook scheme strikes a flexible trade-off between pilot overhead and system performance. Furthermore, we develop an adaptive probability update (APU) strategy that further reduces training overhead by dynamically adjusting the codebook size at each stage. Finally, numerical results illustrate that the proposed PU scheme effectively balances training overhead and system performance by relying only on composite end-to-end channel estimation, achieving comparable performance to CSI-based methods with significantly reduced pilot overhead, while the APU scheme further accelerates convergence and maintains robust performance under limited training overhead.