NOMA-Enabled Optimization Framework for Next-Generation Small-Cell IoV Networks Under Imperfect SIC Decoding

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 11; pp. 22442 - 22451
• Main Authors: Khan, Wali Ullah, Li, Xingwang, Ihsan, Asim, Khan, Mohammad Ayoub, Menon, Varun G, Ahmed, Manzoor
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Decoding; Energy efficiency; Interference; Internet of Vehicles; Monte Carlo simulation; multi-objective optimization; Multiple objective analysis; Network latency; NOMA; non-orthogonal multiple access; Nonorthogonal multiple access; Optimization; Quadratic programming; Quality of service; Resource management; sequential quadratic programing; Silicon carbide; sum-capacity
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2021.3091402

To meet the demands of massive connections, diverse quality of services (QoS), ultra-reliable and low latency in the future sixth-generation (6G) Internet-of-vehicle (IoV) communications, we propose non-orthogonal multiple access (NOMA)-enabled small-cell IoV network (SVNet). We aim to investigate the trade-off between system capacity and energy efficiency through a joint power optimization framework. In particular, we formulate a nonlinear multi-objective optimization problem under imperfect successive interference cancellation (SIC) detecting. Thus, the objective is to simultaneously maximize the sum-capacity and minimize the total transmit power of NOMA-enabled SVNet subject to individual IoV QoS, maximum transmit power and efficient signal detecting. To solve the nonlinear problem, we first exploit a weighted-sum method to handle the multi-objective optimization and then adopt a new iterative Sequential Quadratic Programming (SQP)-based approach to obtain the optimal solution. The proposed optimization framework is compared with Karush-Kuhn-Tucker (KKT)-based NOMA framework, average power NOMA framework, and conventional OMA framework. Monte Carlo simulation results unveil the validness of our derivations. The presented results also show the superiority of the proposed optimization framework over other benchmark frameworks in terms of system sum-capacity and total energy efficiency.