Multivariate Aggregated NOMA for Resource Aware Wireless Network Communication Security

Abstract

Nonorthogonal Multiple Access (NOMA) is incorporated into the wireless network systems to achieve better connectivity, spectral and energy effectiveness, higher data transfer rate, and also obtain the high quality of services (QoS). In order to improve throughput and minimum latency, a Multivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access (MRRWPBA-NOMA) technique is introduced for network communication. In the downlink transmission, each mobile device's resources and their characteristics like energy, bandwidth, and trust are measured. Followed by, the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e., Multivariate Renkonen Regression functions. Based on the classification, resource and trust-aware devices are selected for transmission. Simulation of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio, throughput, latency, packet loss rate, and energy efficiency, signaling overhead. The simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.