Large Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimization

• URL: http://arxiv.org/abs/2602.13210v1
• Date: Thu, 15 Jan 2026 01:42:39 GMT
• Title: Large Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimization
• Authors: Jie Zheng, Ruichen Zhang, Dusit Niyato, Haijun Zhang, Jiacheng Wang, Hongyang Du, Jiawen Kang, Zehui Xiong,
• Abstract summary: Large language models (LLMs) offer promising tools to enhance reinforcement learning in wireless networks.<n>We propose an LLM-assisted state representation and semantic extraction to enhance the multi-agent reinforcement learning framework.
• Score: 79.27012080083603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Enhancing future wireless networks presents a significant challenge for networking systems due to diverse user demands and the emergence of 6G technology. While reinforcement learning (RL) is a powerful framework, it often encounters difficulties with high-dimensional state spaces and complex environments, leading to substantial computational demands, distributed intelligence, and potentially inconsistent outcomes. Large language models (LLMs), with their extensive pretrained knowledge and advanced reasoning capabilities, offer promising tools to enhance RL in optimizing 6G wireless networks. We explore RL models augmented by LLMs, emphasizing their roles and the potential benefits of their synergy in wireless network optimization. We then examine LLM-enabled RL across various protocol layers: physical, data link, network, transport, and application layers. Additionally, we propose an LLM-assisted state representation and semantic extraction to enhance the multi-agent reinforcement learning (MARL) framework. This approach is applied to service migration and request routing, as well as topology graph generation in unmanned aerial vehicle (UAV)-satellite networks. Through case studies, we demonstrate that our framework effectively performs optimization of wireless network. Finally, we outline prospective research directions for LLM-enabled RL in wireless network optimization.

Related papers

• Prompt-Tuned LLM-Augmented DRL for Dynamic O-RAN Network Slicing [5.62872273155603]
  Large Language Models (LLMs) structure unorganized network feedback into meaningful latent representations.<n>In O-RAN slicing, concepts like SNR, power levels and throughput are semantically related.<n>We introduce a contextualization-based adaptation method that integrates learnable prompts into an LLM-augmented DRL framework.
  arXiv  Detail & Related papers  (2025-05-31T14:12:56Z)
• Large Language Model-enhanced Reinforcement Learning for Low-Altitude Economy Networking [71.83640290222928]
  Low-Altitude Economic Networking (LAENet) aims to support diverse flying applications below 1,000 meters.<n>Complex decision-making, resource constraints, and environmental uncertainty pose significant challenges to the development of the LAENet.
  arXiv  Detail & Related papers  (2025-05-27T11:25:42Z)
• DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey [68.74626395093496]
  Reinforcement learning (RL)-based large language models (LLMs) have attracted widespread attention for their capabilities in multimodal data understanding.<n>The open-source DeepSeek models are famous for their innovative designs, such as large-scale pure RL and cost-efficient training.<n>This survey presents a comprehensive exploration of RL-based LLMs in the context of wireless networks.
  arXiv  Detail & Related papers  (2025-03-13T01:59:11Z)
• An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning [13.3347292702828]
  This paper proposes a framework called Autonomous Reinforcement Coordination (ARC) for a SemCom-enabled SAGIN.<n>ARC decomposes orchestration into two tiers, utilizing LLMs for high-level planning and RL agents for low-level decision-making.
  arXiv  Detail & Related papers  (2025-02-22T11:53:34Z)
• NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models [11.015852090523229]
  Large language models (LLMs) have revolutionized various domains by leveraging their sophisticated natural language understanding capabilities.<n>This paper presents NETORCHLLM, a wireless NETwork ORCHestrator LLM framework that seamlessly orchestrates diverse wireless-specific models.<n>A comprehensive framework is introduced, demonstrating the practical viability of our approach.
  arXiv  Detail & Related papers  (2024-12-13T12:48:15Z)
• WDMoE: Wireless Distributed Mixture of Experts for Large Language Models [68.45482959423323]
  Large Language Models (LLMs) have achieved significant success in various natural language processing tasks. We propose a wireless distributed Mixture of Experts (WDMoE) architecture to enable collaborative deployment of LLMs across edge servers at the base station (BS) and mobile devices in wireless networks.
  arXiv  Detail & Related papers  (2024-11-11T02:48:00Z)
• DRL Optimization Trajectory Generation via Wireless Network Intent-Guided Diffusion Models for Optimizing Resource Allocation [58.62766376631344]
  We propose a customized wireless network intent (WNI-G) model to address different state variations of wireless communication networks. Extensive simulation achieves greater stability in spectral efficiency and variations of traditional DRL models in dynamic communication systems.
  arXiv  Detail & Related papers  (2024-10-18T14:04:38Z)
• An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models [16.3772708546698]
  We provide an overview of machine learning (ML)-enabled optimization for RIS-aided 6G networks. Different from existing studies, this work further discusses how large language models (LLMs) can be combined with RL to handle network optimization problems.
  arXiv  Detail & Related papers  (2024-05-09T03:07:59Z)
• Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence [76.96698721128406]
  Mobile edge computing (MEC) considered a novel paradigm for computation and delay-sensitive tasks in fifth generation (5G) networks and beyond. This paper provides a comprehensive research review on free-enabled RL and offers insight for development.
  arXiv  Detail & Related papers  (2022-01-27T10:02:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.