Diffusion Models for Wireless Transceivers: From Pilot-Efficient Channel Estimation to AI-Native 6G Receivers
This addresses the bottleneck of pilot-efficient channel estimation for wireless transceivers in 6G systems, though it appears incremental as an adaptation of existing diffusion models to a new domain.
The paper tackles channel estimation in large-scale OFDM wireless systems by formulating it as a generative AI problem using diffusion models, showing potential to improve efficiency and cooperate with traditional methods, with a proof-of-concept case study demonstrating enhanced receiver performance.
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.