Next-Generation Wi-Fi Networks with Generative AI: Design and Insights

TL;DR

This paper explores how generative AI can revolutionize Wi-Fi network design and performance, introducing models and frameworks that optimize network parameters in complex, high-density environments.

Contribution

It proposes a novel GAI-based Wi-Fi design framework using retrieval-augmented LLMs and diffusion models, advancing network optimization techniques.

Findings

Effective in high-density scenarios

Improves network performance and optimization

Demonstrates potential of GAI in wireless networks

Abstract

Generative artificial intelligence (GAI), known for its powerful capabilities in image and text processing, also holds significant promise for the design and performance enhancement of future wireless networks. In this article, we explore the transformative potential of GAI in next-generation Wi-Fi networks, exploiting its advanced capabilities to address key challenges and improve overall network performance. We begin by reviewing the development of major Wi-Fi generations and illustrating the challenges that future Wi-Fi networks may encounter. We then introduce typical GAI models and detail their potential capabilities in Wi-Fi network optimization, performance enhancement, and other applications. Furthermore, we present a case study wherein we propose a retrieval-augmented LLM (RA-LLM)-enabled Wi-Fi design framework that aids in problem formulation, which is subsequently solved using a generative diffusion model (GDM)-based deep reinforcement learning (DRL) framework to optimize various network parameters. Numerical results demonstrate the effectiveness of our proposed algorithm in high-density deployment scenarios. Finally, we provide some potential future research directions for GAI-assisted Wi-Fi networks.