Power allocation for enhancing energy efficiency in unmanned aerial vehicle networks

The non-orthogonal multiple access (NOMA) wireless networks are a robust multi-access mechanism that serves multiple clients accessing the same resource block simultaneously. The fifth-generation wireless networks offer huge efficiency in spectrum utilization, which can be exploited to deploy NOMA for LOS communication of unmanned aerial vehicles (UAVs). Previous research has extensively analyzed various aspects of NOMA-UAV communication systems, including user access fairness, coverage, maximizing system capacity, and total energy efficiency. However, only a few researchers have focused on maximizing the EE of NOMA-UAV wireless networks with user quality of service (QoS) constraints. This paper proposes a fuzzy logic-based crossover-based coati optimization algorithm for maximizing the energy efficiency of NOMA-UAV, along with user scheduling. The main objective of this model is to offer a solution to the joint energy efficiency and user QoS scheduling problem. The fuzzy decision-making strategy optimizes the energy efficiency (EE) of NOMA-UAV by selecting appropriate power and time resources. Additionally, the crossover-based coati optimization algorithm transforms the subchannel allocation problem into a two-sided matching procedure. The efficiency of the proposed algorithm is evaluated concerning overall residual energy, number of remaining nodes, and time consumption. The experimental outcomes demonstrate the capability of the proposed model in optimizing the energy efficiency of the NOMA-UAV network by identifying the optimal resource set in terms of time and power while satisfying the clients' QoS. This research introduces a fuzzy logic-based optimization algorithm to maximize energy efficiency in non-orthogonal multiple access (NOMA)-unmanned aerial vehicle (UAV) networks with a focus on user's quality of service (QoS). The algorithm efficiently allocates power and time resources, improving overall energy efficiency while meeting QoS requirements. Experimental results demonstrate the effectiveness of this approach in optimizing energy efficiency for NOMA-UAV networks.image