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).

翻译：深度神经网络（DNN）算法正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，尤其适用于大规模多天线信道。然而，训练此类模型通常需要大量高维信道测量数据集，而这些数据的获取极其困难且成本高昂。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，以生成能准确反映现实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多种下游任务（如信道压缩和波束对准）中的有效性得以证明。与先前的方法（如噪声添加或使用生成对抗网络（GAN））相比，我们基于扩散的数据增强方法在信道压缩方面实现了超过1-2 dB的归一化均方误差（NMSE）增益，在波束成形方面获得了11 dB的信噪比（SNR）提升。