Can multi-agent Q-learning optimize mobile network load balancing?
Multi-Agent Q-Learning for Real-Time Load Balancing User Association and Handover in Mobile Networks
This paper proposes using multi-agent Q-learning (a type of reinforcement learning) to improve user association (which user connects to which base station) and handover (switching connections between base stations) in dense mobile networks, particularly those using millimeter wave technology. The goal is to maximize network throughput while ensuring no base station is overloaded. Two approaches are presented: a centralized approach where a central load balancer makes decisions for all users, and a distributed approach where users and base stations negotiate connections through a matching game. Both methods incorporate a handover cost to minimize disruptive connection switching.
Key points for LLM-based multi-agent systems: The distributed approach demonstrates a communication-efficient method for coordinating agent actions using local information, a key consideration for scaling multi-agent systems. The use of Q-learning with load balancing constraints illustrates how reinforcement learning can be applied to multi-agent scenarios with complex dependencies and limitations. The handover cost function provides an example of how to incorporate real-world system costs into a multi-agent reinforcement learning framework. The dynamic nature of the environment (user mobility, channel variations) highlights the importance of adaptation in multi-agent systems, and how online learning can address this challenge.