Performance of SVM technique for DoA Estimation in 5G mm-Wave band

Abstract

Applying Machine Learning algorithms in wireless communication has shown increasing interest due to the increase of demand on capacity, the increase of the number of users, and equipment sharing the limited frequency spectrum resources. Also, the need for a reduction in power consumption at base stations and the optimization of radio coverage make ML an attractive and promising technique. In this paper, we investigate the usage of Support Vector Machine (SVM) technique for Direction of Arrival (DoA) estimation in the millimeter-wave band. The objective is to predict the location of a user in a given area by analyzing the received signals at an array of antennas, using an SVM-based model. The first phase of this technique consists of the training phase that aims to identify the characteristics of each class, and that is based on a set of training samples. The second phase consists of testing the trained model using a set of samples/users. We have carried out a set of simulations based on the developed model. The results are promising in terms of the accuracy of determining the DoA, taking into consideration a channel with noise and multipath.