Enhancing QoS and energy efficiency through hybrid heuristic and metaheuristic techniques in ultra dense small cell deployment

In an extremely dense network, the utilization of energy-efficient small cells contributes to the development of a wireless infrastructure that is both greener and better for the environment. In the present cell frameworks, there is a developing interest for enormous network and higher information rates, which small cell networks plan to satisfy. By decisively conveying small cells and improving their use, energy utilization can be fundamentally diminished. The emphasis is on accomplishing ideal asset use and amplifying energy effectiveness, guaranteeing that the small cells work at their maximum capacity while limiting their natural effect. This approach lines up with the rising requirement for manageable and eco-accommodating arrangements in the media communications industry. This paper presents a methodology that consolidates cross breed heuristic and metaheuristic procedures for the arrangement of super thick small cells. The primary objective is to enhance the network’s energy efficiency and quality of service (QoS). By shrewdly upgrading the position and boundaries of small cells, we can improve the general organization execution, guarantee solid network, and limit energy utilization. Our multi-objective artificial flora optimization (MOAFO) algorithm aids in user clustering by enabling the efficient offloading of data traffic from macro cells and an increase in the number of users in small cells. Thusly, it prompts upgraded information throughput for large scale cell clients. The assurance of ideal small cell areas is brought out through the modified anopheles search (MAS) calculation, which considers different plan imperatives. The quality of small base station deployment is also enhanced by our use of a Siamese neural network (SNN) to characterize the traffic distribution model. To evaluate the performance of our proposed methodology, we conduct extensive simulations under different scenarios. These simulations demonstrate the effectiveness of our approach in achieving improved energy efficiency and meeting QoS constraints.