Impact of Channel Estimation on Load Balancing in 5G Networks

With the proliferation of mobile devices and the Internet of Things (IoT) in 5G networks, it is crucial to make the most of the limited spectrum resources. The growing number of mobile users and the need for excellent communication service quality necessitate the implementation of ultra-dense heterogeneous networks (HetNets), composed of macro, micro, Pico, and femto cells. In HetNets, several cell types offer varying degrees of cell coverage and system capacity. For their adaptability in deployment and superior signal quality, small cells are a popular choice. A macrocell distributes its workload across numerous hotspot zones to accommodate a growing number of users with high traffic demands. The objective of this research is to examine the impact of channel conditions in the Het Nets on convergence based on load balancing techniques. In order to identify load imbalances, an LB algorithm continuously checks the network parameters. When user endpoints (UEs) in overloaded cells request connectivity, an optimization algorithm identifies potential base stations (BSs) to remove from the network. We methodically examine and graphically represent the findings, providing significant insights into the performance of the suggested strategy in various circumstances. In this research, we strictly aim to determine the transmission rate in the rural area scenario, both before and after the implementation of load balancing mechanisms such as Max-SINR, Q Learning, and Deep Q Learning. The best transmission rate achieved would be around 4.52 in the Deep Q Learning algorithm.