An Overview on LLM-based Resource Allocation for Wireless Communications

Nowadays, thanks to advances in machine learning (ML), deep learning (DL), and deep reinforcement learning (DRL), intelligent resource allocation has become an active area of research. However, these techniques are task-specific, requiring model retraining whenever the communication environment changes. To address this issue, large language models (LLMs) have emerged as a promising solution. LLMs, pre-trained on a large amount of data, possesses a significant background knowledge and high generalization cabability. This allows LLM-based resource allocation approaches to generate reasonable outputs without the need for task-specific model design or retraining. However, the use of LLMs still present challenges such as high latency, battery life, scarce bandwidth, and security, necessitating research on techniques that can address these issues. In order to enable practical deployment of LLM-based resource allocation methods, careful consideration of aforementioned challenges is needed. To provide insight into the use of LLMs for wireless resource allocation, this paper presents the fundamentals of LLM, recent research trends, challenges, and future research directions.