Machine Learning Based Optimal Modulation Format Prediction for Physical Layer Network Planning

Physical layer network design and planning process is a cumbersome one. It includes laying out all possible combinations of modulation formats, fiber types, forward error correction codes, channel spacing, etc., conducting exhaustive simulations and lab experiments to come up with carefully tuned engineering rules, and finally using these approximate models to propose transmission feasibility. Besides being cumbersome, there are two fundamental issues in conventional network planning approach, firstly it almost exclusively offers conservative design, leading to resource underutilization, and secondly it's not scalable - neither from planning viewpoint nor computationally - to next-generation highly granular and flexible networks. Machine learning, an artificial intelligence toolset, may be applied to solve aforementioned issues by allowing data-driven model development, and consequent transmission quality prediction. While network planning is an extensive topic, in this paper, we focus on neural network based modulation format classification, autonomously identifying best possible modulation format for a given link configuration.