Machine learning methods for digital signal separation

Abstract

This research explores the ability of machine learning to perform signal separation of an Ethernet style encoded, full-duplex communication. Typical signal separation currently requires an active tap of the communication line, followed by a recombination and retransmission of the data. The purpose of this research is to study a passive approach to the acquisition of data from a full-duplex signal. The machine learning model used in this research is a long-short-term memory recurrent neural network (LSTM-RNN), typically expressed as LSTM. The results show that the LSTM was largely successful in recreating the transmission signal from the measured data points, though the separated signals of the full-duplex Ethernet protocol have not yet been tested using a manual decoding method.