Application of machine learning in 5G to extract prior knowledge of the underlying structure in the interference channel matrices

Abstract: The data traffic has been growing drastic over the past few years due to digitization and new technologies that are introduced to the market, such as autonomous cars. In order to meet this demand, the MIMO-OFDM system is used in the fifth generation wireless network, 5G. Designing the optimal wireless network is currently the main research within the area of telecommunication. In order to achieve such a system, multiple factors has to be taken into account, such as the suppression of interference from other users. A traditional method called linear minimum mean square error filter is currently used to suppress the interferences. To derive such a filter, a selection of parameters has to be estimated. One of these parameters is the ideal interference plus noise covariance matrix. By gathering prior knowledge of the underlying structure of the interference channel matrices in terms of the number of interferers and their corresponding bandwidths, the estimation of the ideal covariance matrix could be facilitated. As for this thesis, machine learning algorithms were used to extract these prior knowledge. More specifically, a two or three hidden layer feedforward neural network and a support vector machine with a linear kernel was used. The empirical findings implies promising results with accuracies above 95% for each model.