Generative AI-Driven Phase Control for RIS-Aided Cell-Free Massive MIMO Systems
This work addresses the challenge of efficient phase control for RIS-aided wireless communication systems, offering a computationally efficient solution that could enhance network performance, though it appears incremental as it builds on existing diffusion models applied to a specific domain.
This paper tackles the problem of optimizing phase shifts in RIS-aided cell-free massive MIMO systems under practical constraints like imperfect CSI and spatial correlation, using generative AI models. The results show that the proposed GCDM matches the sum spectral efficiency of a near-optimal expert algorithm with reduced computational overhead, while GCDIM achieves comparable performance with a 98% reduction in computation time.
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.