Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Kalpesh K. Patel, Malay Chakraborty, Ekant Sharma, Sandeep Kumar Singh

TL;DR

This work tackles RIS phase optimization for RIS-aided cell-free mMIMO under imperfect CSI and spatial correlation by proposing two GenAI-based techniques, GCDM and GCDIM, that condition diffusion-based phase generation on dynamic channel state information. GCDM learns the conditional distribution of optimal RIS phases from expert data and can match the performance of traditional expert algorithms while drastically reducing computation time. GCDIM further accelerates generation through DDIM-based non-Markovian sampling, achieving comparable sum SE with about 98% less computation. The results indicate that GenAI-driven phase design can enable real-time RIS optimization in large-scale cell-free mMIMO systems, with substantial practical impact for 6G-era deployments.

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.

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

TL;DR

This work tackles RIS phase optimization for RIS-aided cell-free mMIMO under imperfect CSI and spatial correlation by proposing two GenAI-based techniques, GCDM and GCDIM, that condition diffusion-based phase generation on dynamic channel state information. GCDM learns the conditional distribution of optimal RIS phases from expert data and can match the performance of traditional expert algorithms while drastically reducing computation time. GCDIM further accelerates generation through DDIM-based non-Markovian sampling, achieving comparable sum SE with about 98% less computation. The results indicate that GenAI-driven phase design can enable real-time RIS optimization in large-scale cell-free mMIMO systems, with substantial practical impact for 6G-era deployments.

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 reduction in computation time, underscoring its potential for efficient phase optimization in RIS-aided cell-free mMIMO systems.
Paper Structure (10 sections, 1 theorem, 19 equations, 2 figures, 2 algorithms)

This paper contains 10 sections, 1 theorem, 19 equations, 2 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Achievable Spectral Efficiency
  4. Problem Formulation
  5. RIS Phase Optimization With GenAI
  6. Motivation of using the GenAI based approaches
  7. GCDM Based RIS Phase Optimization
  8. GCDIM Based RIS Phase Optimization
  9. Numerical Results and Analysis
  10. Conclusion

Key Result

Lemma 1

For the RIS-assisted cell-free mMIMO system employing LMMSE-based conjugate beamforming, the closed-form expression for the achievable SE is given by where the effective signal-to-interference-plus-noise ratio (SINR) for user $k$, denoted $\Delta_k$, is defined as Solving each term in Gamma_k, the closed-form SINR expression at the $k$th user can be obtained as Chien2022RISCellFreeourpaper

Figures (2)

  • Figure 1: Proposed GenAI framework for RIS phase optimization.
  • Figure 2: a) Sum SE versus AP transmit power, b) Training loss convergence for different learning rates, c) Comparison of the execution time across different algorithms.

Theorems & Definitions (1)

  • Lemma 1