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

arXiv: https://arxiv.org/abs/2502.02287 · HTML: https://mlsanity.com/paper/2502.02287/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 4 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023)
- [What Can Transformers Learn In-Context? A Case Study of Simple Function Classes](https://mlsanity.com/paper/2208.01066/) (2022)
- [DC3: A learning method for optimization with hard constraints](https://mlsanity.com/paper/2104.12225/) (2021)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?](https://mlsanity.com/paper/1902.02647/) (2019)
- [Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission](https://mlsanity.com/paper/2404.09134/) (2024)
- [Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects](https://mlsanity.com/paper/2212.09668/) (2022)

## Built on by (most cited first, 9 of 9)

- [Splitwise: Collaborative Edge-Cloud Inference for LLMs via Lyapunov-Assisted DRL](https://mlsanity.com/paper/2512.23310/) (2025)
- [Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows](https://mlsanity.com/paper/2512.20640/) (2025)
- [Bridging 6G IoT and AI: LLM-Based Efficient Approach for Physical Layer's Optimization Tasks](https://mlsanity.com/paper/2602.06819/) (2026)
- [BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding](https://mlsanity.com/paper/2603.18855/) (2026)
- [Accuracy-Delay Trade-Off in LLM Offloading via Token-Level Uncertainty](https://mlsanity.com/paper/2602.07958/) (2026)
- [A Wireless World Model for AI-Native 6G Networks](https://mlsanity.com/paper/2603.25216/) (2026)
- [WARA: Toward Automated Wireless Optimization Research with Closed-Loop LLM Agents](https://mlsanity.com/paper/2608.14573/) (2026)
- [A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks](https://mlsanity.com/paper/2608.14694/) (2026)
- [Agentic Autoresearch for Cell-Edge Power Control: Radically Redefining the Researcher's Role](https://mlsanity.com/paper/2608.26093/) (2026)

## Abstract (arXiv)

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.