A proposed cell selection and handover optimization using TAOWOA in self-organized LTE networks

Abstract

The handover procedure is critical for a smooth connection in a Long Term Evolution Advanced (LTE-A) network. However, when the number of user equipment (UE) in a cell grows, the performance of the handover method suffers. This study focuses on identifying the best target cell or evolved Node B (eNB) to address this problem. The target cell in this method is selected using multi-objective functions like Reference Signal Received Quality (RSRQ), Reference Signal Received Power (RSRP), and uplink Signal to Interference Plus Noise Ratio (SINR). The UE from the loaded cell would provide optimum target eNB after eNB selection. Research has integrated a Tent chaotic map, Adaptive inertia weight, Opposition-based learning into the Whale Optimization Algorithm (TAOWOA). Integration has been made to enhance the likelihood of looking for optimal global solutions. The performance of the expected handover scheme was improved by lowering the call dropping ratio (CDR), call blocking ratio (CBR), handover failure, and handover ping-pong as well as increasing throughput and energy efficiency, according to simulation findings. Proposed scheme gives better results than previous research in terms of call blocking and dropping probabilities as well as failures, throughput, and ping-pong handovers.