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

TL;DR

This paper explores the application of diffusion models, a type of generative AI, to improve channel estimation and transceiver design in wireless OFDM systems, highlighting their potential to enhance AI-native 6G receivers.

Contribution

It introduces the use of diffusion models for wireless transceiver design, demonstrating their potential to improve channel estimation and receiver performance in OFDM systems.

Findings

Diffusion models can effectively handle rough initial channel estimates.

DM-based methods show promise in enhancing wireless receiver performance.

The paper provides a proof-of-concept case study for DMs in wireless communications.

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.