Large Language Models for Wireless Communications: From Adaptation to Autonomy

TL;DR

This paper reviews how large language models can be adapted and developed to create intelligent, autonomous wireless communication systems with enhanced reasoning, adaptability, and efficiency.

Contribution

It introduces the concept of wireless-specific foundation models and autonomous agentic LLMs, highlighting recent advances and future research directions in wireless communications.

Findings

LLMs enable adaptive and intelligent wireless communication solutions.

Development of wireless-specific foundation models improves efficiency and versatility.

Autonomous LLMs facilitate reasoning and coordination in wireless networks.

Abstract

The emergence of large language models (LLMs) has revolutionized artificial intelligence, offering unprecedented capabilities in reasoning, generalization, and zero-shot learning. These strengths open new frontiers in wireless communications, where increasing complexity and dynamics demand intelligent and adaptive solutions. This article explores the role of LLMs in transforming wireless systems across three key directions: adapting pretrained LLMs for communication tasks, developing wireless-specific foundation models to balance versatility and efficiency, and enabling agentic LLMs with autonomous reasoning and coordination capabilities. We highlight recent advances, practical case studies, and the unique benefits of LLM-based approaches over traditional methods. Finally, we outline open challenges and research opportunities, including multimodal fusion, collaboration with lightweight models, and self-improving capabilities, charting a path toward intelligent, adaptive, and autonomous wireless networks.