XL-ChannelDiff: An Efficient Diffusion-Based Multi-Domain Near-Field Channel Extrapolation Framework for XL-MIMO Systems

Accurate channel state information (CSI) acquisition is essential for unleashing the performance gains of extremely large-scale multiple-input multiple-output (XL-MIMO) systems. However, in near-field regions, CSI acquisition is much more challenging than in the far field due to the high-dimensional channel representation and spherical wavefront propagation. To address this, in this paper, we propose an efficient multi-domain near-field channel extrapolation framework for XL-MIMO systems. Leveraging the conditional denoising diffusion implicit model (CDDIM), our approach enables accurate channel extrapolation across the antenna, frequency, and spatial domains. Specifically, we design a physics-aware CDDIM backbone that incorporates position-embedded patch tokenization and a mask-guided multi-head attention mechanism, enabling the model to exploit position-dependent channel correlations induced by near-field spherical-wave propagation. To ensure high-fidelity extrapolation, we incorporate a Wasserstein GAN (WGAN) discriminator that provides adversarial supervision to the CDDIM during both the training and reverse sampling phases. Additionally, a RePaint-style refinement scheme is introduced to optimize the sampling trajectory, further boosting extrapolation accuracy. Extensive experiments demonstrate the superiority of the proposed framework, achieving superior extrapolation accuracy and robust generalization across diverse domains, varied configurations, and severe masking conditions.