AHybrid Technique for Smooth Handover in Wireless Networks

Abstract

Mobile nodes (MNs) can access the internet through different wireless network interfaces such as wireless fidelity (WiFi), worldwide interoperability for microwave access (WiMAX), and cellular networks like long-term evolution (LTE), fifth-generation (5G) networks, etc. During an ongoing session, if the mobile user moves out of the coverage of one base station (BS) and enters into the coverage of another BS, then his continuous connectivity is maintained using handover. The handover process ensures the seamless switching of MNs among multiple networks without any service degradation. The handover process consists of three phases: handover triggering, network selection, and handover execution. Handover should be triggered at an appropriate time to provide a better quality of experience (QoE) to the mobile customers as well as to avoid mobility-related problems such as unnecessary handovers and handover ping-pongs. Moreover, the handover should be performed with the best available network which can fulfill the requirements of both the user and the system. In this thesis, handover triggering and network selection techniques have been developed to enhance overall network performance. The research work presented in this thesis is divided into four phases: In the first phase, a hybrid predictive handover technique based on long short-term memory (LSTM) and support vector machine (SVM) models has been proposed. A proactive handover technique reduces the handover latency and signaling overhead by predicting handover in advance. The selection of the best network with minimum handover latency provides seamless connectivity to the users. LSTM is used to predict the parameters of MNs such as location coordinates, speed, reference signal received power (RSRP), and reference signal received quality (RSRQ) at the next time step based on their values at previous time steps. The output of LSTM is passed as input to the SVM for the selection of the most appropriate network. The performance of the proposed approach is verified on