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LLM-Empowered Resource Allocation in Wireless Communications Systems

Woongsup Lee, Jeonghun Park

TL;DR

This work investigates using large language models (LLMs) for resource allocation in a two-link wireless system, aiming to maximize spectral or energy efficiency under power constraints. It proposes a few-shot prompt-based framework that maps normalized channel gains to transmit powers, with a simple binary power-control fallback to improve reliability. Performance evaluation shows the LLM-based scheme can reach up to about 96% of the optimal energy efficiency with a modest number of prompts, and a hybrid approach with binary control yields additional gains; among tested models, CodeLLaMA-7B often performs best, while larger models offer diminishing returns and can fail with many prompts. The study highlights practical considerations, including latency, model selection, training data needs, and interpretability, and argues that sLLMs with edge AI could enable scalable, near-optimal LLM-enabled wireless control in real deployments.

Abstract

The recent success of large language models (LLMs) has spurred their application in various fields. In particular, there have been efforts to integrate LLMs into various aspects of wireless communication systems. The use of LLMs in wireless communication systems has the potential to realize artificial general intelligence (AGI)-enabled wireless networks. In this paper, we investigate an LLM-based resource allocation scheme for wireless communication systems. Specifically, we formulate a simple resource allocation problem involving two transmit pairs and develop an LLM-based resource allocation approach that aims to maximize either energy efficiency or spectral efficiency. Additionally, we consider the joint use of low-complexity resource allocation techniques to compensate for the reliability shortcomings of the LLM-based scheme. After confirming the applicability and feasibility of LLM-based resource allocation, we address several key technical challenges that remain in applying LLMs in practice.

LLM-Empowered Resource Allocation in Wireless Communications Systems

TL;DR

This work investigates using large language models (LLMs) for resource allocation in a two-link wireless system, aiming to maximize spectral or energy efficiency under power constraints. It proposes a few-shot prompt-based framework that maps normalized channel gains to transmit powers, with a simple binary power-control fallback to improve reliability. Performance evaluation shows the LLM-based scheme can reach up to about 96% of the optimal energy efficiency with a modest number of prompts, and a hybrid approach with binary control yields additional gains; among tested models, CodeLLaMA-7B often performs best, while larger models offer diminishing returns and can fail with many prompts. The study highlights practical considerations, including latency, model selection, training data needs, and interpretability, and argues that sLLMs with edge AI could enable scalable, near-optimal LLM-enabled wireless control in real deployments.

Abstract

The recent success of large language models (LLMs) has spurred their application in various fields. In particular, there have been efforts to integrate LLMs into various aspects of wireless communication systems. The use of LLMs in wireless communication systems has the potential to realize artificial general intelligence (AGI)-enabled wireless networks. In this paper, we investigate an LLM-based resource allocation scheme for wireless communication systems. Specifically, we formulate a simple resource allocation problem involving two transmit pairs and develop an LLM-based resource allocation approach that aims to maximize either energy efficiency or spectral efficiency. Additionally, we consider the joint use of low-complexity resource allocation techniques to compensate for the reliability shortcomings of the LLM-based scheme. After confirming the applicability and feasibility of LLM-based resource allocation, we address several key technical challenges that remain in applying LLMs in practice.
Paper Structure (15 sections, 6 figures)

This paper contains 15 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Basic Principle of LLM and Its Application in Resource Allocation
  3. Principle of LLM
  4. Conventional Resource Allocation Strategies
  5. Resource Allocation Using LLM
  6. LLM-based Resource Allocation
  7. System Model and Considered Resource Allocation
  8. Proposed Resource Allocation based on LLM
  9. Performance Evaluation
  10. Research Challenges
  11. Latency and Computation Time
  12. Optimized LLM Architecture
  13. Training Methodology
  14. Interpretability and Explainability
  15. Conclusions

Figures (6)

  • Figure 1: Comparison between different approaches for resource allocation.
  • Figure 2: Considered system model and procedure for LLM-based resource allocation.
  • Figure 3: EE vs. number of training samples.
  • Figure 4: SE vs. number of training samples.
  • Figure 5: EE vs. number of training samples for larger LLM models.
  • ...and 1 more figures