采用生成式人工智能的下一代 Wi-Fi 网络:设计与见解

Jingyu Wang, Xuming Fang, Dusit Niyato, Tie Liu
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引用次数: 0

摘要

生成式人工智能(GAI)以其在图像和文本处理方面的强大功能而著称,在未来无线网络的设计和性能提升方面也大有可为。在本文中,我们将探讨 GAI 在下一代 Wi-Fi 网络中的变革潜力,利用其先进功能应对关键挑战并提高整体网络性能。我们首先回顾了主要几代 Wi-Fi 的发展历程,并说明了未来 Wi-Fi 网络可能遇到的挑战。然后,我们介绍了典型的 GAI 模型,并详细介绍了它们在 Wi-Fi 网络优化、性能提升和其他应用中的潜在能力。此外,我们还介绍了一个案例研究,在这个案例研究中,我们提出了一个支持检索增强 LLM(RA-LLM)的 Wi-Fi 设计框架,该框架有助于问题的提出,随后使用基于生成扩散模型(GDM)的深度强化学习(DRL)框架来优化各种网络参数。数值结果证明了我们提出的算法在高密度部署场景中的有效性。最后,我们为 GAI 辅助的 Wi-Finetworks 提供了一些潜在的未来研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Next-Generation Wi-Fi Networks with Generative AI: Design and Insights
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.
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