CoDiPhy: A General Framework for Applying Denoising Diffusion Models to the Physical Layer of Wireless Communication Systems

Generative models, including denoising diffusion models (DM), are gaining attention in wireless applications due to their ability to learn complex data distributions. In this paper, we propose CoDiPhy, a novel framework that leverages conditional denoising diffusion models to address a wide range of wireless physical layer problems. A key challenge of using DM is the need to assume or approximate Gaussian signal models. CoDiPhy addresses this by incorporating a conditional encoder as a guidance mechanism, mapping problem observations to a latent space and removing the Gaussian constraint. By combining conditional encoding, time embedding layers, and a U-Net-based main neural network, CoDiPhy introduces a noise prediction neural network, replacing the conventional approach used in DM. This adaptation enables CoDiPhy to serve as an effective solution for a wide range of detection, estimation, and predistortion tasks. We demonstrate CoDiPhy's adaptability through two case studies: an OFDM receiver for detection and phase noise compensation for estimation. In both cases, CoDiPhy outperforms conventional methods by a significant margin.