Open Access

ARTICLE

Multivariate Aggregated NOMA for Resource Aware Wireless Network Communication Security

V. Sridhar¹, K.V. Ranga Rao², Saddam Hussain^3,*, Syed Sajid Ullah⁴, Roobaea Alroobaea⁵, Maha Abdelhaq⁶, Raed Alsaqour⁷

1 ECE Department, Vidya Jyothi Institute of Technology, India
2 CSE Department, Neil Gogte Institute of Technology, India
3 School of Digital Science, Universiti Brunei Darussalam, JalanTungku Link, Gadong BE1410, Brunei Darussalam
4 Department of Information and Communication Technology, University of Agder (UiA), N-4898 Grimstad, Norway
5 Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia
6 Department of Information Technology, College of Computer and Information Sciences, Princess Nourahbint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
7 Department of Information Technology, College of Computing and Informatics, Saudi Electronic University, 93499, Riyadh, Saudi Arabia

* Corresponding Author: Saddam Hussain. Email:

Computers, Materials & Continua 2023, 74(1), 1693-1708. https://doi.org/10.32604/cmc.2023.028129

Received 03 February 2022; Accepted 05 April 2022; Issue published 22 September 2022

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.

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Keywords

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