Mitigating network adaptation and QoT prediction challenges in WDM networks

Abstract The capacity and efficiency of optical communication networks have been completely transformed by wavelength division multiplexing (WDM) technology, which allows many wavelengths to be transmitted simultaneously over a single optical fibre. Conventional QoT prediction is based on analytical models that consider physical layer characteristics including dispersion, optical power and signal-to-noise ratio. But these models frequently oversimplify complex real-world situations, which reduces their accuracy for modern high-speed WDM networks. A data-driven solution is provided by machine learning(ML), which may boost the accuracy of QoT predictions by utilising real-time measurements, historical data and a variety of network situations. The creation of a ML-based framework for QoT prediction is investigated in the current research. This research proposes an effective ML-based routing computation model that uses a non-linear autoregressive recurrent neural network (ML-RCNA-RNN) to ensure QoT for every wavelength channel in high-capacity and high-speed WDM networks. Through simulations, more accurate QoT metrics, such as bit error rate (BER) 68.42 %, QoT prediction accuracy (Q-Factor) 5.9 %, network adaption time (ms) 48.3 %, latency (ms) 0.28 % and throughput (Gbps) 14.29 %, have been obtained compared to conventional QoT predictions. These results were obtained using Gaussian noise Python simulation (GNPy). As a result, the proposed framework that makes use of GNPy demonstrates that it substantially enhances optical communication networks’ performance and dependability. This facilitates the development of high-capacity, low-latency and reliable communication infrastructure, and makes it more adaptable and able to manage the complexity of high-speed WDM optical networks while preserving signal quality in the modern digital era.