Optimization of QoS in 5G optical networks for futuristic high-speed, low-latency applications

Abstract 5G networks support emerging high-speed and low-latency applications, such as remote healthcare and connected cars. Such applications require a consistent quality of service (QoS) to be supported by the underlying optical networks. A combination of spatio-temporal graph neural networks (STGNNs) and twin-delayed deep deterministic policy gradient (TD3), with modifications, is proposed here to solve the problem of QoS optimization through metrics, such as delay, throughput, and jitter. The STGNNs extract the dynamic topology-related features from the network. Modified TD3 helps in learning an adaptive routing policy for optimization of QoS metrics. The proposed model, labelled as spatio-temporal optical network recurrent model with TD3 (STORM-TD3) is validated through simulation. Compared to DDPG, the existing approach, it reduces the end-to-end delay by 9 %, jitter by 1.65 %, maintaining the same level of throughput, and is recommended for deployment in 5G optical networks for QoS optimization.