Diffusion Models for Wireless Transceivers: From Pilot-Efficient Channel Estimation to AI-Native 6G Receivers

TL;DR

The paper addresses the bottleneck of channel estimation in OFDM wireless transceivers by reframing it as a generative task and applying diffusion models to produce high-quality channel estimates from partial, noisy observations. It presents a DM-based transceiver framework that integrates conditional generation with traditional Bayesian inference and signal processing, detailing conditioning mechanisms and practical engineering requirements. A case study demonstrates the repaint inpainting-like approach yielding accurate channel estimates with reduced pilot overhead and controllable latency. The work also maps out open problems and DM-enabled transmission strategies, highlighting the potential for AI-native 6G receivers and guiding future research on topology, data quality, pilot design, and protocol development.

Abstract

With the development of artificial intelligence (AI) techniques, implementing AI-based techniques to improve wireless transceivers becomes an emerging research topic. Within this context, AI-based channel characterization and estimation become the focus since these methods have not been solved by traditional methods very well and have become the bottleneck of transceiver efficiency in large-scale orthogonal frequency division multiplexing (OFDM) systems. Specifically, by formulating channel estimation as a generative AI problem, generative AI methods such as diffusion models (DMs) can efficiently deal with rough initial estimations and have great potential to cooperate with traditional signal processing methods. This paper focuses on the transceiver design of OFDM systems based on DMs, provides an illustration of the potential of DMs in wireless transceivers, and points out the related research directions brought by DMs. We also provide a proof-of-concept case study of further adapting DMs for better wireless receiver performance.

Figures (4)

• Figure 1: Introduction of Diffusion model.
• Figure 2: An overview of the transceiver design based on diffusion model.
• Figure 3: An overview of open problems of applying DMs in wireless transceivers.
• Figure 4: Performance of channel estimation under different pilot density versus denoising steps with and without the repaint pipeline.