The Next Generation Wireless Network Deployment Using Machine Learning Based Multi-Objective Genetic Algorithm
Abstract
6G networks provides ubiquitous connectivity, reduced delay and high-speed gigabit connection. The Introduction of AI to the planning process of 5G beyond networks is crucial to ensure the efficient deployment of cells and the minimization of SINR (signal to interference plus noise ratio). The Multi-Objective Genetic Algorithm (MOGA) to take care of the planning issue in 5G and beyond network organizations. This is accomplished by expanding the already existing 4G and 5G infrastructure. The MOGA endeavors to limit the deployment cost, the interference between the cells and maximize the percentage of the clients being served. This work is the solution for deployment problem in next generation networks. The randomly deployment of the cells decreases the network performance, increases the interference and not effective in terms of deployment cost and leads to Dense Multi-Objective Deployment problem. An optimised deployment strategy is employed in the proposed work to address this issue. This work based on optimized utilization of the network through planning. This decreases the cost of deployment, interference and redundancy. It enhances the coverage capacity and quality of service. This excellent coverage of users which is close to 85% is obtained over existing 4G and 5G infrastructure, thereby reducing the total cost of deployment. The work is compared with the meta-heuristic algorithms. The comparison results shows that the proposed work achieves higher SINR, improved coverage capacity than the meta-heuristic algorithms.
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