Large Artificial Intelligence Model Guided Deep Reinforcement Learning for Resource Allocation in Non Terrestrial Networks

TL;DR

This paper introduces a novel approach where a Large Language Model guides a Deep Reinforcement Learning agent to optimize resource allocation in Non-Terrestrial Networks, significantly improving performance under various weather conditions.

Contribution

It presents a new LLM-guided DRL framework for NTN resource management, enhancing traditional DRL with high-level textual guidance for better performance.

Findings

LAM-DRL outperforms traditional DRL by 40% in nominal weather.

LAM-DRL outperforms traditional DRL by 64% in extreme weather.

The approach improves throughput, fairness, and reduces outage probability.

Abstract

Large AI Model (LAM) have been proposed to applications of Non-Terrestrial Networks (NTN), that offer better performance with its great generalization and reduced task specific trainings. In this paper, we propose a Deep Reinforcement Learning (DRL) agent that is guided by a Large Language Model (LLM). The LLM operates as a high level coordinator that generates textual guidance that shape the reward of the DRL agent during training. The results show that the LAM-DRL outperforms the traditional DRL by 40% in nominal weather scenarios and 64% in extreme weather scenarios compared to heuristics in terms of throughput, fairness, and outage probability.