Conditional Diffusion Model-Enabled Scenario-Specific Neural Receivers for Superimposed Pilot Schemes

TL;DR

This paper introduces a conditional diffusion model to generate scenario-specific channel data, enhancing neural receiver training for superimposed pilot schemes in wireless communications.

Contribution

It proposes a novel scenario-specific channel generation method using conditional diffusion models, improving neural receiver performance with synthetic data augmentation.

Findings

Generated high-fidelity channel samples

Significantly improved neural receiver accuracy

Outperformed GAN-based data augmentation methods

Abstract

Neural receivers have demonstrated strong performance in wireless communication systems. However, their effectiveness typically depends on access to large-scale, scenario-specific channel data for training, which is often difficult to obtain in practice. Recently, generative artificial intelligence (AI) models, particularly diffusion models (DMs), have emerged as effective tools for synthesizing high-dimensional data. This paper presents a scenario-specific channel generation method based on conditional DMs, which accurately model channel distributions conditioned on user location and velocity information. The generated synthetic channel data are then employed for data augmentation to improve the training of a neural receiver designed for superimposed pilot-based transmission. Experimental results show that the proposed method generates high-fidelity channel samples and significantly enhances neural receiver performance in the target scenarios, outperforming conventional data augmentation and generative adversarial network-based techniques.