Signalling overhead minimization aware handover execution using ensemble learning in next generation wireless networks

Upcoming smart intelligent heterogeneous wireless networks (HWNs) and their uses can greatly benefit from the merging of long-term evolution (LTE) sub-6 GHz along with millimeter wave (mmWave) frequencies by boosting the coverage, bandwidth, reliability, seamless connectivity, and high quality of service (QoS). Nevertheless, because of the inability of directed waves in terms of coverage, it is difficult to locate the appropriate mmWave remote radio units (RRUs). Therefore, it is crucial to lessen the burden of the handover signaling processes. In meeting research requirements this paper presents signaling overhead minimization aware handover execution (SOMAHE) model. The SOMAHE model first introduces a novel handover mechanism between LTE and mmWave is presented in this research, followed by a machine learning (ML)-based autonomous handover execution technique. To estimate the handover success rate, the model introduces a feature ensemble learning (FEL) model built using XGBoost (XGB) model that makes use of sampling windows channel data. To conclude, combining FEL into the SOMAHE model reduces signaling overhead while simultaneously increasing the handover success-rate. Experiment results with varying mobile terminals, demonstrate that the SOMAHE model significantly outperforms the existing standard deep q-networks (DQN)-based handover-execution method.