Exploring Mutual Empowerment Between Wireless Networks and RL-based LLMs: A Survey

2025-03-13

AI Summary

Addressing the dual demands of intelligent wireless networking and large language model (LLM) deployment, existing works lack a unified framework to bridge wireless systems with LLMs while tackling cross-domain challenges—including resource scheduling, edge intelligence, distributed training, and real-time decision-making. Method: We propose and formalize a bidirectional empowerment paradigm—“Wireless Networks ↔ Reinforcement Learning–Driven LLMs”—and establish the first communication-computation-semantic co-classification framework. Integrating reinforcement learning, LLM training/inference, wireless resource management, edge/fog computing, and semantic communication, we conduct a systematic survey of technical advances, fundamental bottlenecks, viable pathways, and societal implications. Contribution/Results: Our work defines novel research directions—e.g., joint optimization across physical, computational, and semantic layers—and provides a theoretical foundation, methodological guidance, and an evolutionary roadmap for next-generation intelligent communication systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Multiagent Systems: Agent Communication

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Reinforcement learning (RL)-based large language models (LLMs), such as ChatGPT, DeepSeek, and Grok-3, have gained significant attention for their exceptional capabilities in natural language processing and multimodal data understanding. Meanwhile, the rapid expansion of information services has driven the growing need for intelligence, efficient, and adaptable wireless networks. Wireless networks require the empowerment of RL-based LLMs while these models also benefit from wireless networks to broaden their application scenarios. Specifically, RL-based LLMs can enhance wireless communication systems through intelligent resource allocation, adaptive network optimization, and real-time decision-making. Conversely, wireless networks provide a vital infrastructure for the efficient training, deployment, and distributed inference of RL-based LLMs, especially in decentralized and edge computing environments. This mutual empowerment highlights the need for a deeper exploration of the interplay between these two domains. We first review recent advancements in wireless communications, highlighting the associated challenges and potential solutions. We then discuss the progress of RL-based LLMs, focusing on key technologies for LLM training, challenges, and potential solutions. Subsequently, we explore the mutual empowerment between these two fields, highlighting key motivations, open challenges, and potential solutions. Finally, we provide insights into future directions, applications, and their societal impact to further explore this intersection, paving the way for next-generation intelligent communication systems. Overall, this survey provides a comprehensive overview of the relationship between RL-based LLMs and wireless networks, offering a vision where these domains empower each other to drive innovations.

Problem

• Enhancing wireless networks with RL-based LLMs for intelligent resource allocation.
• Exploring mutual benefits between RL-based LLMs and wireless network infrastructures.
• Addressing challenges in integrating RL-based LLMs with wireless communication systems.

Innovation

• RL-based LLMs enhance wireless communication systems
• Wireless networks support LLM training and deployment
• Mutual empowerment drives next-generation intelligent systems