Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling
This addresses the problem of costly channel modeling for wireless communication system designers, offering a more efficient method, though it appears incremental as it adapts existing diffusion models to a specific domain.
The paper tackles the challenge of high-quality wireless channel data scarcity by proposing a diffusion model-based approach for synthesizing channel realizations from limited data, showing stable training and generation of diverse, high-fidelity samples compared to GAN-based methods.
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.