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Generating High Dimensional User-Specific Wireless Channels using Diffusion Models

Taekyun Lee, Juseong Park, Hyeji Kim, Jeffrey G. Andrews

TL;DR

This work tackles the scarcity of high-dimensional, user-specific wireless channel data required for DNN-based MIMO tasks by proposing a conditional denoising diffusion model (cDDIM) that generates beamspace channels $\mathbf{H}_{\mathrm{v}}$ conditioned on the UE position $\mathbf{x}$. By learning $p(\mathbf{H}_{\mathrm{v}}|\mathbf{x})$ from limited measurements and producing calibrated synthetic samples, the approach enables large augmented datasets while preserving the spatial multipath structure. The method yields substantial gains on downstream tasks, achieving roughly $1$–$2$ dB NMSE improvements in channel compression and about a $11$ dB SNR boost in site-specific beam alignment over baselines such as Gaussian augmentation or ChannelGAN, with robust generalization to out-of-distribution locations. The paper also provides theoretical insights into diffusion-based learning under low-rank channel assumptions and discusses computational trade-offs, underscoring the practical impact for data-efficient training in mmWave/MIMO systems.

Abstract

Deep neural network (DNN)-based algorithms are emerging as an important tool for many physical and MAC layer functions in future wireless communication systems, including for large multi-antenna channels. However, training such models typically requires a large dataset of high-dimensional channel measurements, which are very difficult and expensive to obtain. This paper introduces a novel method for generating synthetic wireless channel data using diffusion-based models to produce user-specific channels that accurately reflect real-world wireless environments. Our approach employs a conditional denoising diffusion implicit model (cDDIM) framework, effectively capturing the relationship between user location and multi-antenna channel characteristics. We generate synthetic high fidelity channel samples using user positions as conditional inputs, creating larger augmented datasets to overcome measurement scarcity. The utility of this method is demonstrated through its efficacy in training various downstream tasks such as channel compression and beam alignment. Our diffusion-based augmentation approach achieves over a 1-2 dB gain in NMSE for channel compression, and an 11dB SNR boost in beamforming compared to prior methods, such as noise addition or the use of generative adversarial networks (GANs).

Generating High Dimensional User-Specific Wireless Channels using Diffusion Models

TL;DR

This work tackles the scarcity of high-dimensional, user-specific wireless channel data required for DNN-based MIMO tasks by proposing a conditional denoising diffusion model (cDDIM) that generates beamspace channels conditioned on the UE position . By learning from limited measurements and producing calibrated synthetic samples, the approach enables large augmented datasets while preserving the spatial multipath structure. The method yields substantial gains on downstream tasks, achieving roughly – dB NMSE improvements in channel compression and about a dB SNR boost in site-specific beam alignment over baselines such as Gaussian augmentation or ChannelGAN, with robust generalization to out-of-distribution locations. The paper also provides theoretical insights into diffusion-based learning under low-rank channel assumptions and discusses computational trade-offs, underscoring the practical impact for data-efficient training in mmWave/MIMO systems.

Abstract

Deep neural network (DNN)-based algorithms are emerging as an important tool for many physical and MAC layer functions in future wireless communication systems, including for large multi-antenna channels. However, training such models typically requires a large dataset of high-dimensional channel measurements, which are very difficult and expensive to obtain. This paper introduces a novel method for generating synthetic wireless channel data using diffusion-based models to produce user-specific channels that accurately reflect real-world wireless environments. Our approach employs a conditional denoising diffusion implicit model (cDDIM) framework, effectively capturing the relationship between user location and multi-antenna channel characteristics. We generate synthetic high fidelity channel samples using user positions as conditional inputs, creating larger augmented datasets to overcome measurement scarcity. The utility of this method is demonstrated through its efficacy in training various downstream tasks such as channel compression and beam alignment. Our diffusion-based augmentation approach achieves over a 1-2 dB gain in NMSE for channel compression, and an 11dB SNR boost in beamforming compared to prior methods, such as noise addition or the use of generative adversarial networks (GANs).
Paper Structure (20 sections, 1 theorem, 15 equations, 11 figures, 3 tables, 4 algorithms)

This paper contains 20 sections, 1 theorem, 15 equations, 11 figures, 3 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Background & Related works
  3. Contributions
  4. Notation & Organization
  5. Preliminaries and Problem Statement
  6. System Model (Channel Model)
  7. Problem Setup
  8. Proposed Techniques for Synthetic Channel Generation
  9. Capturing Channel Distribution via Denoising Score Matching
  10. Conditional DDIM (cDDIM)
  11. Theoretical Analysis
  12. Complexity Analysis
  13. Visualization and Evaluation
  14. Simulation Setup
  15. Visualizations of the Generated Channel
  16. ...and 5 more sections

Key Result

Proposition 1

(Adopted from Pas:11): Assuming that $\log p(\widetilde{\mathbf{H}}_{\mathrm{v}}|\mathbf{H}_{\mathrm{v}},\mathbf{x})$ is differentiable with respect to $\widetilde{\mathbf{H}}_{\mathrm{v}}$, minimizing $\mathcal{L}_\mathrm{exp}(\mathbf{H}_{\mathrm{v}}|\mathbf{x}; \mathbf{\Theta})$ is equivalent to m

Figures (11)

  • Figure 1: Illustration of the proposed wireless channel generation scenario.
  • Figure 2: The cDDIM model architecture.
  • Figure 3: Inference process: Iteratively adding and denoising Gaussian noise over $T$ iterations to generate a synthetic channel from the conditional UE position input $\mathbf{x}_{\mathrm{v,aug,i}}$.
  • Figure 4: Illustration of the two experiment setups. In Exp ID, both the training and test datasets are within a 100 m radius. In Exp OOD, the test dataset is collected from a donut-shaped region spanning 100 m to 200 m from the base station.
  • Figure 5: Visualization of the magnitude of five randomly selected synthetic/reference channel examples. From left to right, each column shows channel samples generated by cGAN, one-shot U-net, cDDIM trained in the spatial domain, cDDIM, and the reference channel.
  • ...and 6 more figures

Theorems & Definitions (3)

  • Proposition 1
  • proof
  • Remark 1