ABSTRACT
Heterogeneous Networks (HetNets) are introduced to satisfy the demand of network traffic capacity and data rate. It comprises of multi-platform networks with various Radio Access Technologies (RATs). A key design feature for vertical handover decision algorithm is to guarantee seamless handover process between varying wireless access technologies without degrading the Quality of Service (QoS) and Quality of Experience (QoE) of the users. However, the design complexity, high frequent handover rates, and inaccurate handover processes are still existing challenges. As a result, there still exists a level of dissatisfaction in the QoS and QoE delivered to the mobile users. To address this challenge, this paper presents an improved Optimized Deep Residual Neural (iODRN) Algorithm to improve the QoS and QoE to users in HetNets. The approach uses a neural network-based approach for dynamic and adaptive network selection in order to improve the user experience based on network conditions and user requirements. The environmental scenario comprised of diverse networks comprising of Fifth-Generation (5G), Long Term Evolution (LTE), and Wireless Fidelity (Wi-Fi). Simulation results showed that the iODRN outperformed the existing algorithm in terms of throughput, Packet Data Rate (PDR), Packet Loss (PL), Handover Failure Rate (HFR), and latency by 38.4%, 3.6%, 87.4%, 61.5%, and 26.3%, respectively.
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