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

Zhiheng Yu, Jiancheng An, Lu Gan, Chau Yuen

TL;DR

This work tackles RIS-assisted MU-MISO systems under practical CSI constraints by introducing an environment-aware codebook protocol. An offline stage uses statistical CSI and alternating optimization to generate a codebook of discrete RIS configurations corresponding to multiple virtual channels, while an online stage uses UL training to evaluate $Q$ candidate channels and selects the one that maximizes the downlink sum-rate. Theoretical analysis derives a power-scaling law under channel estimation errors, and simulations confirm that the environment-aware codebook outperforms random-codebook approaches and adapts effectively to channel coherence time, with complexity that scales with the training overhead rather than the number of RIS elements. The approach reduces training overhead and remains robust to imperfect CSI, offering a practical pathway to efficient RIS-assisted downlink communications.

Abstract

Reconfigurable intelligent surface (RIS) provides a new electromagnetic response control solution, which can reshape the characteristics of wireless channels. In this paper, we propose a novel environment-aware codebook protocol for RIS-assisted multi-user multiple-input single-output (MU-MISO) systems. Specifically, we first introduce a channel training protocol which consists of off-line and on-line stages. Secondly, we propose an environment-aware codebook generation scheme, which utilizes the statistical channel state information and alternating optimization method to generate codewords offline. Then, in the on-line stage, we use these pre-designed codewords to configure the RIS, and the optimal codeword resulting in the highest sum rate is adopted for assisting in the downlink data transmission. Thirdly, we analyze the theoretical performance of the proposed protocol considering the channel estimation errors. Finally, numerical simulations are provided to verify our theoretical analysis and the performance of the proposed scheme.

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

TL;DR

This work tackles RIS-assisted MU-MISO systems under practical CSI constraints by introducing an environment-aware codebook protocol. An offline stage uses statistical CSI and alternating optimization to generate a codebook of discrete RIS configurations corresponding to multiple virtual channels, while an online stage uses UL training to evaluate candidate channels and selects the one that maximizes the downlink sum-rate. Theoretical analysis derives a power-scaling law under channel estimation errors, and simulations confirm that the environment-aware codebook outperforms random-codebook approaches and adapts effectively to channel coherence time, with complexity that scales with the training overhead rather than the number of RIS elements. The approach reduces training overhead and remains robust to imperfect CSI, offering a practical pathway to efficient RIS-assisted downlink communications.

Abstract

Reconfigurable intelligent surface (RIS) provides a new electromagnetic response control solution, which can reshape the characteristics of wireless channels. In this paper, we propose a novel environment-aware codebook protocol for RIS-assisted multi-user multiple-input single-output (MU-MISO) systems. Specifically, we first introduce a channel training protocol which consists of off-line and on-line stages. Secondly, we propose an environment-aware codebook generation scheme, which utilizes the statistical channel state information and alternating optimization method to generate codewords offline. Then, in the on-line stage, we use these pre-designed codewords to configure the RIS, and the optimal codeword resulting in the highest sum rate is adopted for assisting in the downlink data transmission. Thirdly, we analyze the theoretical performance of the proposed protocol considering the channel estimation errors. Finally, numerical simulations are provided to verify our theoretical analysis and the performance of the proposed scheme.
Paper Structure (16 sections, 15 equations, 4 figures)

This paper contains 16 sections, 15 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. The Proposed Channel Training Protocol
  4. Channel Training-Based Protocol
  5. Off-Line Codebook Design Stage
  6. Virtual Channel Generation Based on Statistical CSI
  7. Alternating Optimization Algorithm
  8. Transmit Power Allocation
  9. RIS RC Optimization
  10. On-Line RIS Configuration Stage
  11. Composite Channel Estimation
  12. Transmit Precoding
  13. Optimal Index Selection
  14. Theoretical Analysis of the Proposed Scheme
  15. Simulation Results
  16. ...and 1 more sections

Figures (4)

  • Figure 1: A RIS-assisted multi-user communication system.
  • Figure 2: The frame structure of the proposed protocol.
  • Figure 3: Achievable rate, $R$ versus the training overhead, $Q$.
  • Figure 4: (a) Sum rate, $R$ versus the transmit power at BS, $P_d$. (b) Sum rate, $R$ versus the number of RIS elements, $N$. (c) Sum rate, $R$ versus the training overhead, $Q$. (d) Effective sum rate, $R_e$ versus the channel coherence time, $T_c$ with perfect CSI.