A retrieval-augmented large language model hybrid framework for adaptive wireless resource allocation: design, simulation, and analysis

Efficient wireless resource allocation is crucial for optimizing energy efficiency (EE) and spectral efficiency (SE) in increasingly complex and dynamic communication environments. Traditional optimization methods often lack real-time adaptability, while standard deep learning approaches face challenges with generalization and data requirements. Large language models (LLMs) offer promising reasoning and few-shot learning capabilities. This paper introduces and evaluates a novel hybrid framework integrating retrieval-augmented generation (RAG) with an LLM for adaptive wireless resource allocation. The framework utilizes a knowledge base of historical channel conditions and corresponding optimal power allocations. For new scenarios, relevant historical cases are retrieved via similarity matching and used to construct context-rich prompts for a pre-trained LLM. Critically, this RAG-LLM approach is hybridized with a conventional binary power allocation scheme, enhancing robustness and providing a degree of decision verification by selecting the better-performing allocation. The framework’s design, including knowledge base construction, prompt engineering, the hybrid decision mechanism, and algorithmic flow, is detailed. Extensive simulations using a two-link interference channel model (over 20,000 channel realizations) demonstrate the framework’s effectiveness. The proposed hybrid method achieves an average EE of approximately 9.65 bits/Hz/J (approaching the theoretical optimum of 10.0 bits/Hz/J for the simulated scenario) and an average SE of approximately 8.1 bits/s/Hz. These results significantly surpass those of pure LLM, conventional binary, and random allocation baselines. A comparative computational complexity analysis highlights the framework’s potential for real-time application, discussing the crucial trade-off between substantial performance gains and increased inference latency compared to simpler baselines.