# CSI Prediction Using Diffusion Models

arXiv: https://arxiv.org/abs/2510.11214 · HTML: https://mlsanity.com/paper/2510.11214/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 26 of its 47 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 26 of 26)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2101.12072/) (2021)
- [The Rise of Diffusion Models in Time-Series Forecasting](https://mlsanity.com/paper/2401.03006/) (2024)
- [Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning](https://mlsanity.com/paper/2009.09967/) (2020)
- [Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems](https://mlsanity.com/paper/2006.11796/) (2020)
- [Five Disruptive Technology Directions for 5G](https://mlsanity.com/paper/1312.0229/) (2013)
- [Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO](https://mlsanity.com/paper/2003.05875/) (2020)
- [CSI-BERT2: A BERT-inspired Framework for Efficient CSI Prediction and Classification in Wireless Communication and Sensing](https://mlsanity.com/paper/2412.06861/) (2024)
- [Addressing the curse of mobility in massive MIMO with Prony-based angular-delay domain channel predictions](https://mlsanity.com/paper/1912.11330/) (2019)
- [On the Asymptotic Mean Square Error Optimality of Diffusion Models](https://mlsanity.com/paper/2403.02957/) (2024)
- [A Comparison of Neural Networks for Wireless Channel Prediction](https://mlsanity.com/paper/2308.14020/) (2023)
- [Csi-LLM: A Novel Downlink Channel Prediction Method Aligned with LLM Pre-Training](https://mlsanity.com/paper/2409.00005/) (2024)
- [LinFormer: A Linear-based Lightweight Transformer Architecture For Time-Aware MIMO Channel Prediction](https://mlsanity.com/paper/2410.21351/) (2024)
- [Time-Varying Channel Prediction for RIS-Assisted MU-MISO Networks via Deep Learning](https://mlsanity.com/paper/2111.04971/) (2021)
- [Machine Learning for Future Wireless Communications: Channel Prediction Perspectives](https://mlsanity.com/paper/2502.18196/) (2025)
- [Spectral Temporal Graph Neural Network for massive MIMO CSI Prediction](https://mlsanity.com/paper/2312.02159/) (2023)
- [Next-slot OFDM-CSI Prediction: Multi-head Self-attention or State Space Model?](https://mlsanity.com/paper/2405.11072/) (2024)
- [Predictor Antenna: A Technique to Boost the Performance of Moving Relays](https://mlsanity.com/paper/2012.10537/) (2020)
- [Reverse Ordering Techniques for Attention-Based Channel Prediction](https://mlsanity.com/paper/2302.00341/) (2023)

## Abstract (arXiv)

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.