CSI Prediction Using Diffusion Models

2025-10-13

AI Summary

To address the challenges of untimely and inaccurate CSI acquisition in wireless communications—caused by high pilot overhead and channel aging—this paper proposes a diffusion-based probabilistic CSI prediction framework. The framework decouples the task into two stages: temporal encoding and diffusion-based generation, marking the first application of diffusion models to channel prediction; it explicitly captures CSI’s stochasticity and multimodal distribution. It supports both autoregressive and sequence-to-sequence inference modes and explores a lightweight architecture eliminating the need for an explicit temporal encoder. Leveraging a U-Net/Transformer hybrid backbone and DDIM-accelerated sampling, it balances generation fidelity with computational efficiency. Experiments across multiple channel datasets demonstrate significant improvements over state-of-the-art methods, effectively mitigating the trade-off between pilot overhead and channel aging, thereby enhancing system reliability and spectral efficiency.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

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 introduce a novel probabilistic framework for CSI prediction based on diffusion models, offering a flexible design that supports integration of diverse prediction schemes. 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. We investigate two inference schemes-autoregressive and sequence-to-sequence- and explore multiple diffusion backbones, including U-Net and Transformer-based architectures. Furthermore, we examine a diffusion-based approach without an explicit temporal encoder and utilize the DDIM scheduling to reduce model complexity. Extensive simulations demonstrate that our diffusion-based models significantly outperform state-of-the-art baselines.

Problem

• Predicting future wireless channel state information accurately
• Addressing stochastic and multimodal nature of wireless channels
• Reducing model complexity while maintaining prediction performance

Innovation

• Diffusion models for probabilistic CSI prediction
• Decomposed temporal encoder and diffusion generator
• Multiple diffusion backbones with DDIM scheduling