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Generative AI-Driven Phase Control for RIS-Aided Cell-Free Massive MIMO Systems
🤖 AI Summary
This work addresses the performance limitations of reconfigurable intelligent surfaces (RIS) in practical scenarios caused by imperfect channel state information (CSI) and spatial correlation. To overcome these challenges, it introduces— for the first time—the conditional diffusion model into RIS phase optimization for cell-free massive MIMO systems. The authors propose a generative conditional diffusion model (GCDM) and its implicit variant (GCDIM), which leverage dynamic CSI to enable end-to-end learning for efficiently generating near-optimal phase configurations. GCDM achieves spectral efficiency comparable to expert-designed algorithms while substantially reducing computational overhead. Moreover, GCDIM further cuts computation time by 98%, enabling ultra-low-complexity control without compromising performance.
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Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Reconfigurable Intelligent Surface
Cell-Free Massive MIMO
Phase Shift Optimization
Imperfect CSI
Spectral Efficiency
Innovation
Methods, ideas, or system contributions that make the work stand out.
Generative AI
Reconfigurable Intelligent Surface (RIS)
Cell-Free Massive MIMO
Diffusion Model
Phase Shift Optimization
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