Competitive Multi-Agent Load Balancing with Adaptive Policies in Wireless Networks

TL;DR

This paper introduces a multi-agent reinforcement learning approach for load balancing in wireless networks, demonstrating significant improvements over single-agent methods in key performance metrics.

Contribution

It proposes a novel MADDPG-AP scheme that considers multiple network metrics and interactions among agents, advancing load balancing strategies in wireless networks.

Findings

Significant latency reduction

Lower packet loss ratio

Faster convergence times

Abstract

Using Machine Learning (ML) techniques for the next generation wireless networks have shown promising results in the recent years, due to high learning and adaptation capability of ML algorithms. More specifically, ML techniques have been used for load balancing in Self-Organizing Networks (SON). In the context of load balancing and ML, several studies propose network management automation (NMA) from the perspective of a single and centralized agent. However, a single agent domain does not consider the interaction among the agents. In this paper, we propose a more realistic load balancing approach using novel Multi-Agent Deep Deterministic Policy Gradient with Adaptive Policies (MADDPG-AP) scheme that considers throughput, resource block utilization and latency in the network. We compare our proposal with a single-agent RL algorithm named Clipped Double Q-Learning (CDQL) . Simulation results reveal a significant improvement in latency, packet loss ratio and convergence time