Diffusion Model for Multiple Antenna Communications

TL;DR

This paper explores the application of diffusion models to multiple antenna communications, proposing a unified framework, categorizing tasks, and demonstrating superior performance in beamforming through simulations.

Contribution

It introduces a novel unified framework for applying diffusion models to multiple antenna tasks and categorizes these tasks into decision-making and generation, highlighting their advantages.

Findings

Diffusion models outperform traditional methods in noisy, interference-heavy scenarios.

Simulation results show superior beamforming learning with diffusion models.

Framework applicable to emerging multiple antenna technologies.

Abstract

The potential of applying diffusion models (DMs) for multiple antenna communications is discussed. A unified framework of applying DM for multiple antenna tasks is first proposed. Then, the tasks are innovatively divided into two categories, i.e., decision-making tasks and generation tasks, depending on whether an optimization of system parameters is involved. For each category, it is conceived 1) how the framework can be used for each task and 2) why the DM is superior to traditional artificial intelligence (TAI) and conventional optimization tasks. It is highlighted that the DMs are well-suited for scenarios with strong interference and noise, excelling in modeling complex data distribution and exploring better actions. A case study of learning beamforming with a DM is then provided, to demonstrate the superiority of the DMs with simulation results. Finally, the applications of DM for emerging multiple antenna technologies and promising research directions are discussed.