Dual-Graph Multi-Agent Reinforcement Learning for Handover Optimization

TL;DR

This paper introduces a decentralized multi-agent reinforcement learning approach using graph neural networks for optimizing handover parameters in cellular networks, leading to improved throughput and robustness.

Contribution

It formulates handover optimization as a Dec-POMDP on a dual graph and proposes TD3-D-MA, a novel MARL method with GNNs for scalable, decentralized decision-making.

Findings

Improves network throughput compared to heuristics and centralized RL.

Demonstrates robustness under topology and traffic changes.

Scales effectively with network size and complexity.

Abstract

HandOver (HO) control in cellular networks is governed by a set of HO control parameters that are traditionally configured through rule-based heuristics. A key parameter for HO optimization is the Cell Individual Offset (CIO), defined for each pair of neighboring cells and used to bias HO triggering decisions. At network scale, tuning CIOs becomes a tightly coupled problem: small changes can redirect mobility flows across multiple neighbors, and static rules often degrade under non-stationary traffic and mobility. We exploit the pairwise structure of CIOs by formulating HO optimization as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) on the network's dual graph. In this representation, each agent controls a neighbor-pair CIO and observes Key Performance Indicators (KPIs) aggregated over its local dual-graph neighborhood, enabling scalable decentralized decisions while preserving graph locality. Building on this formulation, we propose TD3-D-MA, a discrete Multi-Agent Reinforcement Learning (MARL) variant of the TD3 algorithm with a shared-parameter Graph Neural Network (GNN) actor operating on the dual graph and region-wise double critics for training, improving credit assignment in dense deployments. We evaluate TD3-D-MA in an ns-3 system-level simulator configured with real-world network operator parameters across heterogeneous traffic regimes and network topologies. Results show that TD3-D-MA improves network throughput over standard HO heuristics and centralized RL baselines, and generalizes robustly under topology and traffic shifts.