用生成式AI优化智能表面相位，提升无线系统效率。
Generative AI-Driven Phase Control for RIS-Aided Cell-Free Massive MIMO Systems
- 用扩散模型根据实时信道信息生成最优相位配置。
- 新方法在保持高吞吐量的同时，计算开销大幅降低。
- 适合追求高效智能反射的5G/6G系统研究者参考。
本文研究一种生成式人工智能（GenAI）模型，用于在实际约束条件下优化智能反射表面（RIS）辅助的无蜂窝大规模多输入多输出（mMIMO）系统中的RIS相位偏移，这些约束包括不完善的信道状态信息（CSI）和空间相关性。提出两种基于GenAI的方法：生成条件扩散模型（GCDM）和生成条件扩散隐式模型（GCDIM），利用以动态CSI为条件的扩散模型，最大化系统总频谱效率（SE）。为评估性能，将所提方法与传统上可实现近似最优解但计算效率低的专家算法进行对比。仿真结果表明，GCDM达到与专家算法相当的总SE，同时显著降低计算开销；此外，GCDIM在保持相近总SE的基础上，进一步实现98%的计算时间减少，凸显其在RIS辅助无蜂窝mMIMO系统中高效相位优化方面的潜力。
原文摘要 · Abstract (English)
This work investigates a generative artificial intelligence (GenAI) model to optimize the reconfigurable intelligent surface (RIS) phase shifts in RIS-aided cell-free massive multiple-input multiple-output (mMIMO) systems under practical constraints, including imperfect channel state information (CSI) and spatial correlation. We propose two GenAI based approaches, generative conditional diffusion model (GCDM) and generative conditional diffusion implicit model (GCDIM), leveraging the diffusion model conditioned on dynamic CSI to maximize the sum spectral efficiency (SE) of the system. To benchmark performance, we compare the proposed GenAI based approaches against an expert algorithm, traditionally known for achieving near-optimal solutions at the cost of computational efficiency. The simulation results demonstrate that GCDM matches the sum SE achieved by the expert algorithm while significantly reducing the computational overhead. Furthermore, GCDIM achieves a comparable sum SE with an additional $98\%$ reduction in computation time, underscoring its potential for efficient phase optimization in RIS-aided cell-free mMIMO systems.
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