Large Language Models in Wireless Communications: Applications and Challenges

Abstract

As 6G networks advance toward higher levels of autonomy and intelligence, the demand for sophisticated multimodal data processing in communication systems is growing exponentially. Conventional localized AI models encounter significant generalization bottlenecks when handling cross-layer network operations and dynamic resource allocation. To overcome these limitations, this paper systematically investigates the application frameworks of large language models (LLMs) in wireless communication systems—spanning from physical-layer protocols to high-layer network management—while critically evaluating the associated deployment challenges. Drawing on a comprehensive review of prominent literature published over the past three years, this study empirically assesses the performance of diverse LLM architectures across three key domains: physical-layer protocol parsing, network-layer resource allocation, and service orchestration. Results demonstrate that LLMs yield substantial improvements in end-to-end semantic communication, standardized protocol interpretation, and intelligent network resource scheduling. Nevertheless, practical deployment remains severely hindered by the computational constraints of edge devices and prohibitively high inference latency. We conclude that the co-design of lightweight, telecom-specific large language models (Telecom-LLMs) and distributed inference mechanisms constitutes a pivotal evolutionary pathway toward realizing endogenous intelligence in future wireless communication systems.