부산대학교 도서관

This paper investigates the framework of cross-entropy (CE) based clustering and beamforming for mmWave-non-orthogonal multiple access (NOMA) system taking into consideration the impact of imperfect successive interference cancellation (SIC). For the design of clustering and beamforming, we adopt CE based machine learning algorithm that has the objective to obtain the statistical parameters by minimizing the cross-entropy between optimal and sampling distributions. By using CE based clustering, the number of clusters can be adjusted to strike a balance between the inter-cluster interference and intra-cluster interference introduced by imperfect SIC. Furthermore, the inter-cluster interference induced by spatial beamforming is further reduced using CE based beamforming, which can significantly enhance the system performance of mmWave-NOMA. Based on the result, we compute the power allocation by dividing it into the intra-cluster and inter-cluster power allocation problems. In particular, we derive the optimal intra-cluster power allocation in a closed form and obtain the condition to guarantee the minimum rate requirements of all the users. We next solve the inter-cluster power allocation using convex optimization technique. Data-intensive simulation results illustrate that our proposed algorithm outperforms the conventional algorithm such as $K$ -mean based clustering and the number of clusters can be controlled using CE based clustering algorithm.