Modular Simulation Environment Towards OTN AI-based Solutions
This work addresses a data availability problem for researchers and developers in optical transport and 5G networking, though it is incremental as it builds on existing simulation approaches.
The paper tackles the lack of large datasets for developing AI-based solutions in next-generation networks like 5G and OTN by proposing a modular simulation environment to generate high-volume, high-fidelity datasets, which can improve the accuracy and adaptation of machine learning models to real-life networking traffic.
The current trend for highly dynamic and virtualized networking infrastructure made automated networking a critical requirement. Multiple solutions have been proposed to address this, including the most sought-after machine learning ML-based solutions. However, the main hurdle when developing Next Generation Network is the availability of large datasets, especially in 5G and beyond and Optical Transport Networking (OTN) traffic. This need led researchers to look for viable simulation environments to generate the necessary volume with highly configurable real-life scenarios, which can be costly in setup and require subscription-based products and even the purchase of dedicated hardware, depending on the supplier. We aim to address this issue by generating high-volume and fidelity datasets by proposing a modular solution to adapt to the user's available resources. These datasets can be used to develop better-aforementioned ML solutions resulting in higher accuracy and adaptation to real-life networking traffic.