Generative Artificial Intelligence for Mobile Communications: A Diffusion Model Perspective

doi:10.1109/MCOM.001.2400284

Generative Artificial Intelligence for Mobile Communications: A Diffusion Model Perspective

2024-01-01

0

PRE

AI

DOI：10.1109/MCOM.001.2400284

Original

Original request for help

Save

Abstract

En 中文

This article highlights the potential of a prominent generative artificial intelligence (GAI) method, namely diffusion model (DM), for mobile communications. First, we propose a DM-driven communication architecture which introduces two key paradigms, that is, conditional DM, and DM-driven deep reinforcement learning (DRL), for wireless data generation and communication management, respectively. Then, we discuss the key advantages of the DM-driven communication paradigms. To elaborate further, we explore DM-driven channel generation mechanisms for channel estimation, extrapolation, and feedback in multiple-input multiple-output (MIMO) systems. We showcase the numerical performance of conditional DM using the accurate DeepMIMO channel datasets, revealing its superiority in generating high-fidelity channels and mitigating unforeseen distribution shifts in sophisticated scenes. Furthermore, several DM-driven communication management designs promising to deal with imperfect channels and task-oriented communications are conceived. To inspire future research developments, we highlight the potential applications and open research challenges of DM-driven communications. Code is available at https://github.com/ xiaoxiaxusummer/GAI_COMM/.

Keywords:

Wireless communication

Noise reduction

Channel estimation

Noise

Data models

Mobile communication

Data collection

Stochastic processes

Extrapolation

Trajectory

Generative Artificial Intelligence for Mobile Communications: A Diffusion Model Perspective

Abstract

Journal

Organization