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

arXiv: https://arxiv.org/abs/2602.11226 · HTML: https://mlsanity.com/paper/2602.11226/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 9 of 9)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [CDDM: Channel Denoising Diffusion Models for Wireless Semantic Communications](https://mlsanity.com/paper/2309.08895/) (2023)
- [A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks](https://mlsanity.com/paper/2303.14320/) (2023)
- [Diffusion Models as Network Optimizers: Explorations and Analysis](https://mlsanity.com/paper/2411.00453/) (2024)
- [Analysis and Optimization of RIS-Assisted Cell-Free Massive MIMO NOMA Systems](https://mlsanity.com/paper/2407.04006/) (2024)
- [Rate-Splitting for Cell-Free Massive MIMO: Performance Analysis and Generative AI Approach](https://mlsanity.com/paper/2409.14702/) (2024)
- [Energy-Efficient Cell-Free Network Assisted by Hybrid RISs](https://mlsanity.com/paper/2210.01440/) (2022)
- [Multi-agent Reinforcement Learning-based Joint Precoding and Phase Shift Optimization for RIS-aided Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2404.14092/) (2024)

## Built on by (most cited first, 1 of 1)

- [Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Models](https://mlsanity.com/paper/2606.31334/) (2026)

## Abstract (arXiv)

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.