arXiv:2510.10895v2 Announce Type: replace Abstract: Medium Access Control (MAC) protocols, essential for wireless networks, are typically manually configured. While deep reinforcement learning (DRL)-based protocols enhance task-specified network performance, they suffer from poor generalizability and resilience, demanding costly retraining to adapt to dynamic environments. To overcome this limitation, we introduce a game-theoretic LLM-empowered multi-agent DRL (MARL) framework, in which the uplink transmission between a base station and a varying number of user equipments is modeled as a dynam
The increasing sophistication of LLMs and the recognition of DRL's limitations in dynamic network environments are driving innovation towards more generalizable and adaptive protocols.
This research integrates advanced AI into fundamental network infrastructure, potentially enabling highly autonomous and efficient wireless communication systems that adapt without constant human intervention.
Traditional manually configured or DRL-based MAC protocols, which lack adaptability and generalizability, may be superseded by LLM-empowered agentic systems capable of dynamic, real-time optimization.
- · AI agents developers
- · Wireless network providers
- · Telecommunications equipment manufacturers
- · Smart infrastructure developers
- · Legacy network protocol developers
- · Manual network optimization services
Wireless networks achieve significantly improved efficiency and resilience, adapting autonomously to fluctuating conditions.
The complexity and cost of deploying and managing large-scale wireless networks decrease due to self-optimizing capabilities.
New applications and services requiring highly dynamic and autonomous network coordination become viable, accelerating the deployment of fully autonomous systems across various sectors.
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Read at arXiv cs.AI