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 models (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 approach significantly improves over prior methods, such as adding noise or using generative adversarial networks (GANs), especially in scenarios with limited initial measurements.

翻译：深度神经网络（DNN）算法正逐渐成为未来无线通信系统中许多物理层和MAC层功能的重要工具，特别是在大规模多天线信道领域。然而，训练此类模型通常需要大量高维信道测量数据集，而这些数据的获取非常困难且成本高昂。本文提出了一种利用基于扩散的模型生成合成无线信道数据的新方法，以产生能准确反映真实世界无线环境的用户特定信道。我们的方法采用条件去噪扩散隐式模型（cDDIM）框架，有效捕捉用户位置与多天线信道特性之间的关系。我们以用户位置作为条件输入，生成高保真度的合成信道样本，从而创建更大的增强数据集以克服测量数据稀缺的问题。该方法的实用性通过其在训练信道压缩和波束对准等多种下游任务中的有效性得到验证。相较于先前的方法（如添加噪声或使用生成对抗网络（GAN）），我们的方法取得了显著改进，尤其是在初始测量数据有限的场景下。