Hybrid Cuckoo Search with Salp Swarm Optimization for Spectral and Energy Efficiency Maximization in NOMA System

Non-Orthogonal Multiple Access (NOMA) holds the efficiency of enabling 5G communication. Due to the faster emergence of smart devices and their correlated applications, there is a huge demand for data traffic to increase the data rate. As a result, these raising demands of the users and the restricted spectrum will minimize the energy and spectral efficiency of the wireless network. There are two major measures like spectral and energy efficiency in Fifth-Generation (5G) communication models technically analyzed in this paper. The main intent of this paper is to develop a hybrid meta-heuristic algorithm for maximizing the spectral and energy efficiency of NOMA, thus avoiding low latency communication. The proposed model integrates two well-performing meta-heuristic algorithms like Salp Swarm optimization algorithm (SSA) and Cuckoo Search Algorithm (CS) for attaining the energy and spectral efficiency maximization in NOMA. The proposed hybrid meta-heuristic algorithm called Cuckoo Levy-based SSA (CL-SSA) is developed to optimize the parameters like beamforming vectors and time allocation ratio at the base station and relay. As the conventional optimization algorithm spectral efficiency of the system reaches maximum. The mean of the proposed CL-SSA for spectral efficiency is 23%, 2.8%, 54%, 32%, and 10% increased than Cuckoo Search (CS), Salp Swarm optimization (SSA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Fire Fly algorithm (FF), respectively. The experimental result shows that the proposed CL-SSA maximizes the spectral efficiency and energy efficiency than conventional techniques like Cuckoo Search (CS), Salp Swarm optimization (SSA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Fire Fly algorithm (FF) in developed NOMA model.