Dual-Graph Multi-Agent Reinforcement Learning for Handover Optimization

• URL: http://arxiv.org/abs/2603.24634v1
• Date: Wed, 25 Mar 2026 08:48:48 GMT
• Title: Dual-Graph Multi-Agent Reinforcement Learning for Handover Optimization
• Abstract summary: We propose a TD3-D-MA in an ns-3 system-level simulator configured with real network operator parameters across heterogeneous traffic regimes.<n>Results show that TD3-D-MA generalizes network throughput overologies standard HO3-Dly.
• Score: 39.348940144274515
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G [60.29349053952935]
  We introduce the Telecom World Model(TWM) concept, an architecture for learned, action-conditioned, uncertainty-aware modeling of telecom system dynamics.<n>We propose a three-layer architecture, comprising a field world model for spatial environment prediction, a control/dynamics world model for action-conditioned Key Performance Indicator (KPI) trajectory prediction, and a telecom foundation model layer for intent translation and orchestration.
  arXiv  Detail & Related papers  (2026-04-08T09:41:58Z)
• Factored Value Functions for Graph-Based Multi-Agent Reinforcement Learning [0.0]
  Credit assignment is a core challenge in multi-agent reinforcement learning (MARL)<n>We introduce the Diffusion Value Function (DVF), a factored value function for GMDPs that assigns to each agent a value component by diffusing rewards over the influence graph with temporal discounting and spatial attenuation.<n>We show that DVF is well-defined, admits a Bellman fixed point, and decomposes the global discounted value via an averaging property.<n>We propose Diffusion A2C (DA2C) and a sparse message-passing actor, Learned DropEdge GNN (LD-GNN), for learning decentralised algorithms under communication costs.
  arXiv  Detail & Related papers  (2026-01-16T16:11:50Z)
• Resource Allocation in Hybrid Radio-Optical IoT Networks using GNN with Multi-task Learning [11.833896722352568]
  This paper addresses the problem of dual-technology scheduling in hybrid Internet of Things (IoT) networks that integrate Optical NeuralOWC and Radio Frequency (RF)<n>We propose a supervised multi-task learning architecture combining a two-stage Graph Embedding with Transformer (DGET) framework.<n>The proposed framework achieves near-optimal scheduling with over 90% classification accuracy, reduces computational complexity, and demonstrates higher robustness under partial channel observability.
  arXiv  Detail & Related papers  (2025-10-29T15:02:28Z)
• Power Grid Control with Graph-Based Distributed Reinforcement Learning [60.49805771047161]
  This work advances a graph-based distributed reinforcement learning framework for real-time, scalable grid management.<n>A Graph Neural Network (GNN) is employed to encode the network's topological information within the single low-level agent's observation.<n>Experiments on the Grid2Op simulation environment show the effectiveness of the approach.
  arXiv  Detail & Related papers  (2025-09-02T22:17:25Z)
• Scalable spectral representations for multi-agent reinforcement learning in network MDPs [13.782868855372774]
  A popular model for multi-agent control, Network Markov Decision Processes (MDPs) pose a significant challenge to efficient learning. We first derive scalable spectral local representations for network MDPs, which induces a network linear subspace for the local $Q$-function of each agent. We design a scalable algorithmic framework for continuous state-action network MDPs, and provide end-to-end guarantees for the convergence of our algorithm.
  arXiv  Detail & Related papers  (2024-10-22T17:45:45Z)
• FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression [55.992528247880685]
  Decentralized training faces significant challenges regarding system design and efficiency. We present FusionLLM, a decentralized training system designed and implemented for training large deep neural networks (DNNs) We show that our system and method can achieve 1.45 - 9.39x speedup compared to baseline methods while ensuring convergence.
  arXiv  Detail & Related papers  (2024-10-16T16:13:19Z)
• Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning [60.94661435297309]
  The scale of mobile networks makes it challenging to optimize antenna parameters using manual intervention or hand-engineered strategies. We propose a new multi-agent reinforcement learning algorithm to optimize mobile network configurations globally. We empirically demonstrate the performance of the algorithm on an antenna tilt tuning problem and a joint tilt and power control problem in a simulated environment.
  arXiv  Detail & Related papers  (2023-01-20T17:06:34Z)
• Feudal Multi-Agent Reinforcement Learning with Adaptive Network Partition for Traffic Signal Control [44.09601435685123]
  Multi-agent reinforcement learning (MARL) has been applied and shown great potential in traffic signal control. Previous work partitions the traffic network into several regions and learns policies for agents in a feudal structure. We propose a novel feudal MARL approach with adaptive network partition.
  arXiv  Detail & Related papers  (2022-05-27T09:02:10Z)
• Multi-path Neural Networks for On-device Multi-domain Visual Classification [55.281139434736254]
  This paper proposes a novel approach to automatically learn a multi-path network for multi-domain visual classification on mobile devices. The proposed multi-path network is learned from neural architecture search by applying one reinforcement learning controller for each domain to select the best path in the super-network created from a MobileNetV3-like search space. The determined multi-path model selectively shares parameters across domains in shared nodes while keeping domain-specific parameters within non-shared nodes in individual domain paths.
  arXiv  Detail & Related papers  (2020-10-10T05:13:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.