CSI Prediction Using Autoregressive Conditional Diffusion Models

Paper i proceeding, 2026

Acquiring accurate channel state information (CSI) is critical for reliable and efficient wireless communication, but challenges such as high pilot overhead and channel aging hinder timely and accurate CSI acquisition. CSI prediction, which forecasts future CSI from historical observations, offers a promising solution. Recent deep learning approaches, including recurrent neural networks and Transformers, have achieved notable success but typically learn deterministic mappings, limiting their ability to capture the stochastic and multimodal nature of wireless channels. In this paper, we propose a novel CSI prediction scheme based on diffusion models. We decompose the CSI prediction task into two components: a temporal encoder, which extracts channel dynamics, and a diffusion-based generator, which produces future CSI samples autoregressively. Extensive simulations demonstrate that our diffusion-based models significantly outperform state-of-the-art baselines.