The Performance of Supervised Machine Learning Based Relay Selection in Cooperative NOMA

This paper aims to exploit the benefits of supervised machine learning algorithms in resolving joint relay selection and non-orthogonal multiple access (NOMA) transmission in cooperative networks. Both relay selection and NOMA are capable to increase the system throughput. The optimization of the joint mixed-integer relay selection under the NOMA transmission problem is hard to be solved for achieving the global maximum of the system throughput. To simplify this complexity, supervised and unsupervised machine learning algorithms have a strong profile in dealing with hard optimization problems. The supervised learning algorithms have shown promising results in relay selection, and one of the widely applied supervised learning algorithms is the support vector machine (SVM). Therefore, in this paper, relay selection is approached as an SVM multi-class classification problem. The main concept of the SVM classification algorithm is to optimize the parameters of the SVM classifiers by training the classifiers with a large set of system realizations. The major advantage of this method is that the training stage can be performed offline as the optimizing of the SVM classifiers parameters requires high processing power, then the trained SVM multi-class classifiers can be used directly with no more training required during the system operation. Simulation results validate that the performance of the proposed supervised learning-based scheme is close to that of the global optimum exhaustive search relay selection scheme and outperforms the other available schemes. In addition, the proposed scheme is considerably simpler than the exhaustive search scheme, primarily when the number of relays is large.