Improving of 5G Wireless Networks using Optimization Method

Due to the emerging of modern evolution of the Fifth Generation (5G) wireless technology and the growth of data traffic to meet various communication requirements of future applications. Routing and path planning in 5G wireless networks is one of the challenges brought about by the variation of network features like traffic load and topology which may differ in stochastic and time varying manner. However, as the mobile users' growth, there is a need to efficient routing strategy for shortest path to forward user requests to the servers to reach services. To meet this aim, an adaptive Meta-heuristic Ant Colony Optimization (ACO) algorithm has proposed in this paper. ACO based on swarm intelligence techniques that has considered inspired algorithm by imitating the sociological attitude related to ants crowd. ACO like the other evolutionary algorithms based on population; it initiates with random solutions of the population. This work was implemented using NS3 simulation tool with different scenarios and parameters. The computational results have shown that the ACO method is better to find the optimal solution in 5G networks than the traditional inelastic way. The simulation outcome indicates the efficiency of the proposed method where the packet delivery ratio increased while the drop packets ratio and the transmission delay reduced.