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

2025-02-04

AI Summary

Addressing the NP-hard resource allocation problem under QoS constraints in dynamic wireless environments, existing deep learning (DL) approaches struggle to model discrete decision variables and hard constraints, while requiring frequent retraining and exhibiting poor adaptability. This paper proposes LLM-RAO—the first large language model (LLM)-based optimizer for communication resource optimization—leveraging constraint-aware instruction modeling and prompt tuning to enable zero-shot task transfer and real-time environmental adaptation without retraining. LLM-RAO overcomes fundamental DL limitations in discrete decision-making and strict QoS enforcement, marking the first application of LLMs to constrained wireless resource optimization. Experiments demonstrate that LLM-RAO achieves a 40% performance gain over state-of-the-art DL methods and an 80% improvement over analytical approaches; under dynamic objective switching, it attains 2.9× higher performance than conventional DL solutions.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Planning with Language Models
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Deep learning (DL) has made notable progress in addressing complex radio access network control challenges that conventional analytic methods have struggled to solve. However, DL has shown limitations in solving constrained NP-hard problems often encountered in network optimization, such as those involving quality of service (QoS) or discrete variables like user indices. Current solutions rely on domain-specific architectures or heuristic techniques, and a general DL approach for constrained optimization remains undeveloped. 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.

Problem

• Optimize resource allocation in dynamic wireless environments
• Address constrained NP-hard problems in network optimization
• Enhance adaptability without extensive retraining

Innovation

• LLM for resource allocation
• Prompt-based tuning strategy
• Adaptable without retraining