An Intelligent SDWN Routing Algorithm Based on Network Situational Awareness and Deep Reinforcement Learning

TL;DR

This paper presents an intelligent routing algorithm for software-defined wireless networks that leverages deep reinforcement learning and network situational awareness to improve routing efficiency and stability.

Contribution

It introduces a novel DRL-based routing mechanism with network prediction and situational awareness, enhancing real-time decision-making and convergence stability.

Findings

Outperforms traditional routing in throughput, delay, and packet loss.

Significantly improves convergence speed over Dueling DQN.

Reduces hardware storage requirements for routing decisions.

Abstract

Due to the highly dynamic changes in wireless network topologies, efficiently obtaining network status information and flexibly forwarding data to improve communication quality of service are important challenges. This article introduces an intelligent routing algorithm (DRL-PPONSA) based on proximal policy optimization deep reinforcement learning with network situational awareness under a software-defined wireless networking architecture. First, a specific data plane is designed for network topology construction and data forwarding. The control plane collects network traffic information, sends flow tables, and uses a GCN-GRU prediction mechanism to perceive future traffic change trends to achieve network situational awareness. Second, a DRL-based data forwarding mechanism is designed in the knowledge plane. The predicted network traffic matrix and topology information matrix are treated as the environment for DRL agents, while next-hop adjacent nodes are treated as executable actions. Accordingly, action selection strategies are designed for different network conditions to achieve more intelligent, flexible, and efficient routing control. The reward function is designed using network link information and various reward and penalty mechanisms. Additionally, importance sampling and gradient clipping techniques are employed during gradient updating to enhance convergence speed and stability. Experimental results show that DRL-PPONSA outperforms traditional routing methods in network throughput, delay, packet loss rate, and wireless node distance. Compared to value-function-based Dueling DQN routing, the convergence speed is significantly improved, and the convergence effect is more stable. Simultaneously, its consumption of hardware storage space is reduced, and efficient routing decisions can be made in real-time using the current network state information.