Large Artificial Intelligence Model Guided Deep Reinforcement Learning for Resource Allocation in Non Terrestrial Networks
This addresses resource allocation challenges in Non-Terrestrial Networks, offering significant performance gains in communication scenarios, though it is incremental as it builds on existing DRL and LLM methods.
The paper tackles resource allocation in Non-Terrestrial Networks by proposing a Deep Reinforcement Learning agent guided by a Large Language Model, which improves throughput, fairness, and outage probability by 40% in nominal weather and 64% in extreme weather compared to heuristics.
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.