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Machine Learning to Support the Optimization of ATM-Grade Networks

ICAO and the Federal Aviation Administration (FAA) operate an international, satellite-based network in the Caribbean, Central America, and the United States, called MEVA III. The network provides interactive voice and data telecommunication services for air traffic control in the region and, as such, requires high availability and performance. Outages do not only result in a degradation of service, but they also constitute safety risks.While network optimization in networks is typically the result of parameter measurements, more sophisticated and advanced solutions rely on real-time deep packet inspections to derive optimization parameters.However, Air Traffic Control (ATC) networks in the future will be protected by end-to-end encryption algorithms such as TLS or DTLS. That means that deep packet inspection will not be possible anymore, and the data plane can only see up to the transport layer information (port numbers), but not deeper.Different methods for network optimizations are needed. This paper describes an algorithm that uses Machine Learning (ML) methodologies to predict the probability of transmission errors in the satellite link communication. This prediction can then be used to perform such network optimizations. The ML concept described predicts the symbol energy-to-noise power density [energy per bit] of a satellite link.The complete satellite link performance data is combined with associated, regional weather data to create training samples for a regression type machine learning prediction. The training samples contain history and future windows so that the trained neural network is able to perform sequence to sequence predictions to output the predicted future symbol energy-to-noise power density.During the training a Tree Parzen hyperparameter optimizer is used to select the best fitting neural network topology and training parameters like neuron number or optimizer learning rate. Machine learning pipelines allow the execution of necessary data manipulation steps like sample filtering of invalid samples, interpolating missing sample values or necessary normalization.The concept was validated as part of the SINAPSE research project in the MEVA III network. The completed hyperparameter training of 30 models demonstrates that the trend of the energy-to-noise power density can be predicted correctly in many cases. This paper will present and discuss the project results and provide an outlook at the future research planned.