Generative AI-Driven Phase Control for RIS-Aided Cell-Free Massive MIMO Systems

TL;DR

This paper introduces generative AI models, GCDM and GCDIM, to optimize RIS phase shifts in cell-free massive MIMO systems, achieving near-optimal spectral efficiency with significantly reduced computational complexity.

Contribution

The paper proposes two novel generative AI approaches for RIS phase optimization, outperforming traditional algorithms in efficiency while maintaining high spectral efficiency.

Findings

GCDM matches expert algorithm's spectral efficiency.

GCDIM achieves similar spectral efficiency with 98% less computation.

Both models outperform traditional optimization in computational speed.

Abstract

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.