4G LTE Handover Parameter Optimization Using Machine Learning Techniques

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Fallaha, Saly
Breijieh, Alaa
Dweikat, Alaa
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LTE (Long Term Evolution) is a fourth-generation cellular network technology that provides improved performance related to data rate, coverage and capacity compared to legacy cellular systems. In this context, one of the main goals of LTE is to provide fast and seamless handover from one cell to another to meet a strict delay requirement while simultaneously keeping network management simple. Hence, the decision to trigger a handover is a crucial component in the design process of handover, since the success and the efficiency, to a large extent, depends on the accuracy and timeliness of the decision. The design of an efficient and successful handover requires a careful selection of HO parameters and the optimal setting of these. The LTE standard supports two parameters to trigger the handover and select the target cell: hysteresis margin and Time-to-Trigger (TTT). The research topic of this project which is “LTE Handover Parameter Optimization'' focuses on different combinations or settings of HOM and TTT values to evaluate the handover performance based on Reference Signal Received Power (RSRP) measurement within certain deployment scenarios, such as different UE speeds. The main goal of this project is to pick the best hysteresis and time to- trigger combinations to evaluate the performance of LTE handover, with measurements for different handover parameters settings in certain deployment scenarios such as number of handovers, signal-to-interference plus noise ratio (SINR), throughput, a simulation procedure is carried out and an evaluation methodology is applied to analyze the simulation results. To do this the NS-3 has been used, a discrete-event network simulator that is regularly updated and maintained , latter the LTE handover model was used to create topology with a script completely written in C++ using the LTE handover module as described by the NS-3 documentation, the topology consists of eNBs, femtocells, buildings, and UEs followed by propagation model and mobility model. Subsequently, some simulation scenarios using different values for specific variables have been run. Finally, the output data for different case analyzed using MATLAB which is a powerful tool for realistic understanding, then plot the output result and compare it to understand the effect of each parameter.