Leveraging Generative Diffusion Models for Enhanced Beam Alignment in Cell-Free MIMO Systems

In cell-free multiple-input multiple-output (MIMO) systems, beam alignment is critical to achieving high spectral efficiency and reliable communication. However, traditional optimization-based methods often suffer from high computational complexity and sensitivity to dynamic channel conditions, especially in decentralized architectures with distributed access points (APs) and mobile users. To address these challenges, a novel scheme for beam alignment optimization that leverages generative diffusion models (GDMs) is proposed in this paper. By learning the underlying distribution of optimal beamforming configurations from historical channel state information (CSI) and user positioning data, the proposed approach generates high-quality precoding and combining matrices that minimize beam alignment errors while maximizing signal-to-noise ratio (SNR). The system model integrates a conditional diffusion process, where CSI and user locations serve as input to guide the generation of beamforming solutions. The framework operates in two phases: an offline training stage that learns the latent distribution of optimal beam alignment, and an online inference stage that rapidly adapts to real-time channel variations. To ensure practicality, the design incorporates power constraints and feedback mechanisms to dynamically refine beam configurations. Simulations demonstrate significant improvements in beam alignment accuracy and communication performance, particularly in high-mobility scenarios.