Large Language Models-Empowered Wireless Networks: Fundamentals, Architecture, and Challenges

TL;DR

This paper explores the integration of large language models into wireless networks, proposing a new LLM-native system architecture, a case study with a DDQN solution, and discussing future challenges.

Contribution

It introduces the concept of LLM-native wireless systems, providing fundamentals, a vision, and a case study with a novel DDQN-based solution for distributed implementation.

Findings

The DDQN-based solution outperforms existing methods.

The paper presents a comprehensive framework for LLM integration in wireless networks.

Open challenges for future research are discussed.

Abstract

The rapid advancement of wireless networks has resulted in numerous challenges stemming from their extensive demands for quality of service towards innovative quality of experience metrics (e.g., user-defined metrics in terms of sense of physical experience for haptics applications). In the meantime, large language models (LLMs) emerged as promising solutions for many difficult and complex applications/tasks. These lead to a notion of the integration of LLMs and wireless networks. However, this integration is challenging and needs careful attention in design. Therefore, in this article, we present a notion of rational wireless networks powered by \emph{telecom LLMs}, namely, \emph{LLM-native wireless systems}. We provide fundamentals, vision, and a case study of the distributed implementation of LLM-native wireless systems. In the case study, we propose a solution based on double deep Q-learning (DDQN) that outperforms existing DDQN solutions. Finally, we provide open challenges.