Handover Decision-Making Algorithm for 5G Heterogeneous Networks

Abstract

The evolution of 5G small cell networks has led to the advancement of vertical handover decision-making algorithms. A mobile terminal (MT) tends to move from one place to another and, as the 5G network coverage is small, user network access will change frequently and lead to a high probability of unnecessary handover, which is a waste of network resources and causes degradation of service quality. This paper aims to reduce the number of unnecessary handovers in 5G heterogeneous networks by proposing a handover decision-making algorithm that integrates the dwelling time prediction technique and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed algorithm reduces the number of unnecessary handovers by estimating the connection time to a small cell network using the dwell time prediction technique. The TOPSIS evaluates the network quality and chooses the best network based on user preference. The result shows that the proposed handover algorithm reduces the number of unnecessary handovers to small cell networks in high-speed scenarios. It also saves the network connection cost by up to 27.51% compared with the TOPSIS-based handover algorithm. As for throughput achievement, the proposed algorithm yields an improvement of 5.12%. The proposed algorithm significantly reduces the number of unnecessary handovers in the high-speed scenario while fulfilling user preferences.

Analytical Model of the Connection Handoff in 5G Mobile Networks with Call Admission Control Mechanisms

“…One of many elements that can help to better optimize resource usage may be the handoff mechanism. Various variations of the handoff mechanism have been the subject of many studies in relation to 5G networks [ 3 , 13 , 14 , 15 , 16 , 17 ]. In [ 3 ], the authors focused on the issue of the distributed-handoff delay and the handover failure of mobile nodes in SDN-based networks.…”

“…This mechanism significantly reduces handover latency in predictive handover while maintaining high prediction accuracy. In [ 15 ], a handover decision-making algorithm was proposed that integrates the dwelling-time prediction technique and the technique for order preference by similarity to ideal solution (TOPSIS). The algorithm presented reduces the number of unnecessary handovers in 5G heterogeneous networks.…”

“…Calculation of the values of the total conditional passing coefficients for assembly g based on ( 6), (15), or (21), depending on the introduced CAC mechanism.…”

Self-Optimization of Handover Control Parameters for 5G Wireless Networks and Beyond

“…Many optimization studies have been conducted for mainly Hysteresis and Time-To-Trigger [3]. Some authors have proposed using soft HCPs such as dwell time or distance [4]- [6] which has been implemented mostly for indoor access point, with dwell time accuracy concern [7]. The current industrial practice of optimizing HCPs involves manual tuning of HCPs by leveraging human experience based on trials and sometimes using vendor predefined values (fixed HCPs values).…”