Performance of SVM technique for DoA Estimation in 5G mm-Wave band
Machine learning has been widely used in binary and multiclass problems for various applications. Application of ML 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. Methods aiming to an intelligent and optimized use of the limited resources are necessary to fill these needs. In this paper, we investigate the usage of artificial intelligence, and in particular the Support Vector Machine (SVM) technique for DoA estimation in the millimeter-wave band. The objective, in the context of this paper, 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. We have considered different configurations where we have varied the training samples set, the antennas array, the width of the zone of the user location. The results are promising in terms of the accuracy of determining the DoA, taking into consideration a channel with noise and multipath. Also, the use of an antenna array with an inter-element spacing of around half-wavelength gives better performance, and the increase in the number of elements of the array enhances the prediction accuracy of the system.