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.

翻译：神经接收机在无线通信系统中已展现出卓越性能，但其有效性通常依赖于获取大规模场景专用信道数据进行训练，而这在实际中往往难以实现。近年来，生成式人工智能模型，特别是扩散模型，已成为合成高维数据的有效工具。本文提出一种基于条件扩散模型的场景专用信道生成方法，该方法能够精确建模以用户位置和速度信息为条件的信道分布。生成的合成信道数据随后被用于数据增强，以优化为叠加导频传输设计的神经接收机的训练。实验结果表明，所提方法能够生成高保真度的信道样本，并显著提升目标场景下神经接收机的性能，其表现优于传统数据增强及基于生成对抗网络的技术。