Applying Graph Neural Network in Deep Reinforcement Learning to Optimize Wireless Network Routing

Abstract

At present, the traffic in wireless sensor networks (WSN) is growing at an extremely fast speed, consuming more and more network resources. This undoubtedly affects the transmission performance of WSN. Good and efficient routing technology is one of the key technologies to solve this problem. Limited by the dynamic network state, traditional routing technology faces some problems such as performance degradation and lack of learning ability. In contrast, Deep Reinforcement Learning (DRL), which has the ability of decision-making and online learning, has a better effect in facing the routing optimization problem. DRL can learn routing strategy online or offline through reinforcement learning mechanism and deep neural network. However, the existing routing models based on DRL use fully connected neural networks or convolutional neural networks, and cannot learn the network topology information. This will lead to the failure of the previously trained routing model in the face of a new network. Therefore, under the background that WSN nodes may fail, resulting in topology changes, this paper combines Graph Neural Network (GNN) with DRL, and proposes GRL-NET intelligent routing algorithm. The algorithm uses GNN instead of conventional neural network to construct DRL Agent. With the help of GNN, GRL-NET can not only learn the complex relationship among network topology, traffic and routing from the perspective of network topology, but also run in a network topology that has never appeared before. In order to evaluate the effect of GRL-NET, several groups of experiments were conducted under different traffic intensity. Experimental results show that GRL-NET can not only learn the best routing strategy, but also keep good results in the never-seen network topology.