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.

翻译：本研究探讨了一种生成式人工智能（GenAI）模型，用于在非理想信道状态信息（CSI）和空间相关性等实际约束下，优化RIS辅助无蜂窝大规模多输入多输出（mMIMO）系统中可重构智能表面（RIS）的相移。我们提出了两种基于GenAI的方法：生成式条件扩散模型（GCDM）和生成式条件扩散隐式模型（GCDIM），利用以动态CSI为条件的扩散模型来最大化系统的总频谱效率（SE）。为评估性能，我们将所提出的基于GenAI的方法与一种专家算法进行了比较，该算法传统上以牺牲计算效率为代价获得接近最优的解。仿真结果表明，GCDM在达到与专家算法相当的总SE的同时，显著降低了计算开销。此外，GCDIM在实现可比总SE的基础上，进一步减少了$98\%$的计算时间，凸显了其在RIS辅助无蜂窝mMIMO系统中进行高效相位优化的潜力。