NOMA-enabled Optimization Framework for Next-generation Small-cell IoV
Networks under Imperfect SIC Decoding
Abstract
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.