Handover management in software-defined 5G small cell networks via long short-term memory

Abstract

5G and beyond communication technologies have started to spread around the world. Higher frequencies lead 5G base stations to have small coverage areas. Besides, the wireless network users have mobility and may move fast among the base stations. Software-defined networking (SDN) is a promising network solution for dynamic and dense networks such as 5G networks. The handover process defines the transfer of mobile users' connections among the base stations and the handover has to happen frequently in ultra-dense networks. In this study, we aim to construct a more robust handover based on long short-term memory (LSTM) with SDN in terms of the number of handover and handover failures. LSTM, linear regression, support vector machine, and tree algorithms performances have been investigated for handover. According to the R-2 values of LSTM, SVM, tree, linear regression results are obtained as 0.998, 0.980, 0.980, and 0.75, respectively. Root mean square error, coefficient of determination (R), mean squared error, and mean absolute deviation statistics prove the improvement of the handover mechanism. In the proposed approach, approximately 30% reduction in the HO failure ratio and 22.22% reduction number of handover have been observed.