Machine Learning-Based Power Allocation Optimization Algorithm for Enhanced CR-NOMA Network

Hybrid non-orthogonal multiple access (NOMA) is a promising scheme to significantly improve communication performance within a 5G mobile network. In this article, a NOMA network is established in the context of underlay cognitive radio (CR) firstly. In addition, the combination of artificial noise (AN) and decode-and-forward (DF) technology is applied at the unmanned aerial vehicle (UAV) relay. We then derive the secrecy outage probability (SOP), which is affected by power allocation factors, to measure the system secrecy performance. Finally, a machine learning (ML) power allocation optimization algorithm is proposed considering both network security and user fairness. To be specific, we employ a revised decision tree (DT) embedded multilayer perceptron (MLP) in the leaf nodes, aiming to utilize the strengths of both ML algorithms effectively and find the optimal groups of systems with various parameters rapidly. The data results demonstrate that the introduced algorithm provides satisfactory predictions and obtains more accurate coefficients compared to other ML algorithms.