GNN-DRL-Based Intelligent Routing and Resource Allocation Algorithms for Multi-Layer Wireless Mesh Network
Lei Xu, Shu Han, Wei Fu, Ziran Zhu, Jing Wu, Xiaorong Zhu

TL;DR
This paper introduces a new algorithm called GraphSAGE-MAPPO that uses AI to improve routing and resource allocation in dynamic wireless mesh networks, especially in emergency communication scenarios.
Contribution
The novel contribution is the GraphSAGE-MAPPO algorithm, which combines graph neural networks and deep reinforcement learning for intelligent routing and resource allocation in dynamic wireless mesh networks.
Findings
GraphSAGE-MAPPO effectively extracts network features and adjusts routing strategies in dynamic environments.
The algorithm demonstrates strong generalization performance for changing network topologies and resource conditions.
Simulation results show improved ability to meet diverse Quality of Service (QoS) requirements in emergency communication scenarios.
Abstract
This research introduces a new intelligent routing and resource allocation algorithm called Graph Sample and Aggregate-Multi-Agent Proximal Policy Optimization (GraphSAGE-MAPPO), which targets dynamic wireless mesh networks like those present in emergency communications. Aiming to address the emergency communication scenario where the network topology changes dynamically and the introduction of Artificial Intelligence (AI) model training services leads to more diverse user services and more dynamic node resource capabilities, a three-dimensional mesh network intelligent routing and resource allocation algorithm, GraphSAGE-MAPPO, based on Graph Neural Networks (GNN) combined with Deep Reinforcement Learning (DRL), is proposed. During the training process, the algorithm first uses GNN as a network feature extraction module to extract the resource capabilities and link status indicators of…
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Taxonomy
TopicsSoftware-Defined Networks and 5G · Mobile Ad Hoc Networks · Advanced Data and IoT Technologies
