Related papers: Large Language Models for Wireless Communications: From Adaptation to Autonomy

Large Language Models for Wireless Communications: From Adaptation to Autonomy

URL: http://arxiv.org/abs/2507.21524v1
Date: Tue, 29 Jul 2025 06:21:10 GMT
Title: Large Language Models for Wireless Communications: From Adaptation to Autonomy
Authors: Le Liang, Hao Ye, Yucheng Sheng, Ouya Wang, Jiacheng Wang, Shi Jin, Geoffrey Ye Li,
Abstract summary: Large language models (LLMs) offer unprecedented capabilities in reasoning, generalization, and zero-shot learning.<n>This article explores the role of LLMs in transforming wireless systems across three key directions.
Score: 47.40285060307752
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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 core 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 of the future.

Related papers

A Survey on Collaborative Mechanisms Between Large and Small Language Models [5.1159419867547085]
Large Language Models (LLMs) deliver powerful AI capabilities but face deployment challenges due to high resource costs and latency.<n>Small Language Models (SLMs) offer efficiency and deployability at the cost of reduced performance.
arXiv Detail & Related papers (2025-05-12T11:48:42Z)
Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap [51.198001060683296]
Large Language Models (LLMs) offer transformative potential to address transportation challenges.<n>This survey first presents LLM4TR, a novel conceptual framework that systematically categorizes the roles of LLMs in transportation.<n>For each role, our review spans diverse applications, from traffic prediction and autonomous driving to safety analytics and urban mobility optimization.
arXiv Detail & Related papers (2025-03-27T11:56:27Z)
Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models [75.4890331763196]
Recent breakthroughs in Large Language Models (LLMs) have led to the emergence of agentic AI systems.<n>LLM-based Agentic RS (LLM-ARS) can offer more interactive, context-aware, and proactive recommendations.
arXiv Detail & Related papers (2025-03-20T22:37:15Z)
DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey [62.697565282841026]
Reinforcement learning (RL)-based large language models (LLMs) have gained significant attention.<n> Wireless networks require the empowerment of RL-based LLMs.<n> Wireless networks provide a vital infrastructure for the efficient training, deployment, and distributed inference of RL-based LLMs.
arXiv Detail & Related papers (2025-03-13T01:59:11Z)
NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models [11.015852090523229]
Large language models (LLMs) have revolutionized various domains by leveraging their sophisticated natural language understanding capabilities.<n>This paper presents NETORCHLLM, a wireless NETwork ORCHestrator LLM framework that seamlessly orchestrates diverse wireless-specific models.<n>A comprehensive framework is introduced, demonstrating the practical viability of our approach.
arXiv Detail & Related papers (2024-12-13T12:48:15Z)
WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence [16.722524706176767]
Large Language Models (LLMs) have sparked interest in their potential to revolutionize wireless communication systems. Existing studies on LLMs for wireless systems are limited to a direct application for telecom language understanding. This paper proposes WirelessLLM, a comprehensive framework for adapting and enhancing LLMs to address the unique challenges and requirements of wireless communication networks.
arXiv Detail & Related papers (2024-05-27T11:18:25Z)
Personalized Wireless Federated Learning for Large Language Models [75.22457544349668]
Large language models (LLMs) have driven profound transformations in wireless networks.<n>Within wireless environments, the training of LLMs faces significant challenges related to security and privacy.<n>This paper presents a systematic analysis of the training stages of LLMs in wireless networks, including pre-training, instruction tuning, and alignment tuning.
arXiv Detail & Related papers (2024-04-20T02:30:21Z)
Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems [57.41621687431203]
Large language models (LLMs) and foundation models have been recently touted as a game-changer for 6G systems. This paper presents a comprehensive vision on how to design universal foundation models tailored towards the deployment of artificial intelligence (AI)-native networks.
arXiv Detail & Related papers (2024-01-30T00:21:41Z)
Large Generative AI Models for Telecom: The Next Big Thing? [7.36678071967351]
Large GenAI models are envisioned to open up a new era of autonomous wireless networks. In this article, we aim to unfold the opportunities that can be reaped from integrating large GenAI models into the Telecom domain.
arXiv Detail & Related papers (2023-06-17T03:45:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.