Channel modelling is essential to designing modern wireless communication systems. The increasing complexity of channel modelling and the cost of collecting high-quality wireless channel data have become major challenges. In this paper, we propose a diffusion model based channel sampling approach for rapidly synthesizing channel realizations from limited data. We use a diffusion model with a U Net based architecture operating in the frequency space domain. To evaluate how well the proposed model reproduces the true distribution of channels in the training dataset, two evaluation metrics are used: $i)$ the approximate $2$-Wasserstein distance between real and generated distributions of the normalized power spectrum in the antenna and frequency domains and $ii)$ precision and recall metric for distributions. We show that, compared to existing GAN based approaches which suffer from mode collapse and unstable training, our diffusion based approach trains stably and generates diverse and high-fidelity samples from the true channel distribution. We also show that we can pretrain the model on a simulated urban macro-cellular channel dataset and fine-tune it on a smaller, out-of-distribution urban micro-cellular dataset, therefore showing that it is feasible to model real world channels using limited data with this approach.

翻译：信道建模对于设计现代无线通信系统至关重要。信道建模日益增长的复杂性以及高质量无线信道数据采集的高昂成本已成为主要挑战。本文提出一种基于扩散模型的信道采样方法，可从有限数据中快速合成信道实现。我们采用基于U-Net架构的扩散模型，在频率空间域中进行运算。为评估所提模型复现训练数据集中信道真实分布的效果，使用两个评价指标：i) 天线域与频率域中归一化功率谱的真实分布与生成分布之间的近似2-瓦瑟斯坦距离；ii) 分布的精确率与召回率指标。结果表明，相较于现有基于生成对抗网络（GAN）方法存在的模式崩溃与训练不稳定问题，我们的扩散方法训练稳定，并能从真实信道分布中生成多样且高保真的样本。此外，我们证明可在模拟的城市宏蜂窝信道数据集上预训练模型，并在较小的、分布外城市微蜂窝数据集上进行微调，从而展示该方法利用有限数据建模真实信道场景的可行性。