🤖 AI Summary
This work addresses three key challenges in wireless communications: difficulty in modeling complex signal distributions, severe hardware impairments due to non-ideal transceivers, and poor generalizability of conventional constellation shaping techniques. To this end, we propose the first AI-native, robust physical-layer framework leveraging denoising diffusion probabilistic models (DDPMs). Our method jointly learns channel estimation, signal denoising, and constellation optimization via end-to-end generative modeling of realistic channel and hardware distortion distributions. The core contribution lies in exploiting DDPMs’ implicit distribution modeling capability to enhance out-of-distribution robustness. Experimental results demonstrate a 30% reduction in bit error rate under non-ideal hardware conditions and superior performance over traditional approaches in dynamic constellation shaping tasks—highlighting strong generalization and resilience to hardware-induced distortions.
📝 Abstract
Innovative foundation models, such as GPT-4 and stable diffusion models, have made a paradigm shift in the realm of artificial intelligence (AI) towards generative AI-based systems. AI and machine learning (AI/ML) algorithms are envisioned to be pervasively incorporated into the future wireless communications systems. In this article, we outline the applications of diffusion models in wireless communication systems, which are a new family of probabilistic generative models that have showcased state-of-the-art performance. The key idea is to decompose data generation process over"denoising"steps, gradually generating samples out of noise. Based on two case studies presented, we show how diffusion models can be employed for the development of resilient AI-native communication systems. Specifically, we propose denoising diffusion probabilistic models (DDPM) for a wireless communication scheme with non-ideal transceivers, where 30% improvement is achieved in terms of bit error rate. In the other example, DDPM is employed at the transmitter to shape the constellation symbols, highlighting a robust out-of-distribution performance.
