An Enhanced Quality of Transmission‐Aware Optical Networking Based on Deep Reinforcement Learning

The core of the optical network is experiencing an increase in traffic demand, driving the need for innovative solutions that may increase transmissions and network bandwidth without significantly increasing service prices. A significant difficulty intrinsic to network reconfiguration is the estimation of quality of transmission (QoT) utilizing measures such as bit‐error‐rate (BER), signal‐to‐noise ratio (SNR), the dispersion of colors, and polarization mode dispersion. Network operators may improve or maintain Quality of Service (QoS) by responding proactively to performance degradation when the predicted QoT of previously established lightpaths is available. By letting network management and control systems self‐learn effective networking rules from real‐world experiences, Deep Reinforcement Learning (DRL) makes autonomous optical networking possible. This paper proposes the Deep Reinforcement Learning‐based QoT on Optical Networking (DRL‐QoT‐ON) that allows the system to anticipate transmission problems and implement fixes, enhancing the network's efficiency and dependability. To make it easier to retrieve and transmit essential information across activities that need various dimensions of network status data, the paper first provides a modular architecture for DRL agents. The system's ability to constantly adapt to changing network circumstances guarantees optimal performance in all situations, made possible by a sophisticated DRL algorithm. The outcomes are improved network performance, lower latency, and better signal quality. The suggested technique works well in ever‐changing optical communication situations since deep learning models are adaptable and can optimize themselves. The proposed method improves optical communication systems' QoT awareness and paves the way for smart and adaptive networking innovations.