Adaptive Resource Allocation Optimization Using Large Language Models in Dynamic Wireless Environments

doi:10.1109/TVT.2025.3572440

Adaptive Resource Allocation Optimization Using Large Language Models in Dynamic Wireless Environments

2025-10-01

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DOI：10.1109/TVT.2025.3572440

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While deep learning (DL) has made notable progress in addressing complex radio access network control challenges, DL has shown limitations in solving constrained NP-hard problems often encountered in network optimization. Moreover, even minor changes in communication objectives demand time-consuming retraining, limiting their adaptability to dynamic environments where task objectives, constraints, environmental factors, and communication scenarios frequently change. To address these challenges, we propose a large language model for resource allocation optimizer (LLM-RAO), a novel approach that harnesses the capabilities of LLMs to address the complex resource allocation problem while adhering to QoS constraints. By employing a prompt-based tuning strategy to flexibly convey ever-changing task descriptions and requirements to the LLM, LLM-RAO demonstrates robust performance and seamless adaptability in dynamic environments without requiring extensive retraining. Simulation results reveal that LLM-RAO achieves up to a 40% performance enhancement compared to conventional DL methods and up to an 80% improvement over analytical approaches. Moreover, in scenarios with fluctuating communication objectives, LLM-RAO attains up to 2.9 times the performance of traditional DL-based networks.

Keywords:

Optimization

Servers

Resource management

Quality of service

IEEE 802.11ax Standard

Vectors

Uplink

Mathematical models

Wireless communication

Tuning

Large language model

resource allocation

in-context learning

prompting

Adaptive Resource Allocation Optimization Using Large Language Models in Dynamic Wireless Environments

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