  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.
