MULTI-OBJECTIVE RESOURCE ALLOCATION OPTIMIZATION IN OVERLAPPING WI-FI AND LTE NETWORKS USING HYBRID METAHEURISTIC ALGORITHMS

Abstract

As one of the key solutions for managing the increasing growth of data traffic, Wi-Fi/LTE overlap networks require advanced resource allocation methods that can simultaneously improve throughput, energy efficiency, and traffic offloading efficiency. The resource allocation problem in these networks is nonlinear, constrained, and multi-objective in nature, and due to user competition and interaction of two heterogeneous technologies, the use of classical optimization methods does not meet practical needs. In this paper, a hybrid meta-heuristic method based on Aquila Optimization Algorithm (AO) and Fosa Optimization Algorithm (FOA) is presented to solve the resource allocation and uplink traffic offloading problem in Wi-Fi/LTE overlap networks. The main idea of the proposed method is to combine the global search power of AO with the local exploitation capability of FOA in order to create an effective balance between exploration and exploitation in the search space. The problem is formulated as a multi-objective optimization model including throughput, energy efficiency, and unloading indices, and time and logical constraints of the system are also considered. Simulation results show that the proposed hybrid method reduces the value of the objective function by about 0.028 and provides better performance compared to independent methods. Also, the system throughput using this method has increased to about 22.4 and the energy efficiency has improved to approximately 20.8. Statistical analysis of the results using the Friedman test also shows that the superiority of the proposed method is statistically significant and is not due to random fluctuations. Overall, the results of this research show that the AO–FOA hybrid method can be used as an efficient, stable, and reliable solution for resource management and traffic unloading in heterogeneous and high-density wireless networks.