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

2025-11-02

AI Summary

Neural receivers rely heavily on scene-specific channel measurements, yet real-world channel data is scarce. To address this, we propose a conditional diffusion model for channel data generation that leverages dynamic prior information—such as user location and velocity—to enable controllable, high-fidelity modeling of channel distributions. This work marks the first application of conditional diffusion models to wireless channel modeling, significantly improving the physical consistency and scene adaptability of synthesized data. The generated samples augment training of neural receivers operating under superimposed pilot transmission, substantially enhancing symbol detection performance under limited real-data regimes. Experiments demonstrate that our method outperforms conventional data augmentation and GAN-based baselines in channel fidelity, generalization across unseen scenarios, and detection accuracy.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Web data generation and simulation
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

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.

Problem

• Generates scenario-specific wireless channel data using conditional diffusion models
• Enhances neural receiver training with synthetic data for superimposed pilot schemes
• Overcomes limited channel data availability through generative AI data augmentation

Innovation

• Conditional diffusion models generate scenario-specific channel data
• Synthetic data enhances neural receiver training via augmentation
• Method outperforms conventional generative adversarial network techniques