Machine Learning based Handover Execution Algorithm for Heterogeneous Wireless Networks

Amalgamation of mmWave and LTE sub-6 provides an advantage in enhancing the reliability, communication bandwidth and optimized coverage of intelligent network and its significant applications; however, identifying the right mmWave RRU (remote Radio units) is one of the main obstacles due to blindness coverage of directional beams. Moreover, mmWave network depends on edge cloud deployment to satisfy the low latency of smart applications. Further, the minimization of energy consumption for handover is necessary due to the limited battery for IoT-device. Hence, it is necessary to minimize the signal overhead in handover process; at first, this research work focuses on the efficient handover mechanism among the mmWave and LTE, later automated handover execution process between mmWave and LTE is developed using XGBoost classification mechanism. The XGBoost algorithm is used predict the handover success rate through channel information based on the sampling window. At last, XGBoost based handover mechanism along with the standard handover mechanism is integrated to minimize the signal overhead and improvise handover success rate. Moreover, performance evaluation is carried out through varying the IoT device and obtained result shows that XGBoost-based handover proves to be better than the existing mechanism of KNN-based handover execution algorithm.