A moving direction prediction-assisted handover scheme in LTE networks

Abstract

The 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) defines a wireless network standard for high packet transmission rate and low packet latency provisions. Handover is one of the important features for helping user equipments (UEs) to roam between LTE networks. However, LTE networks adapt a make-before-break handover procedure, which may cause a brief disconnection, therefore results in the packet transmission delay and packet loss problems. In this paper, we propose a moving direction prediction-assisted handover scheme for LTE networks to lower the number of handovers. We first track the location of user equipments (UEs) to predict their moving direction. By referencing previous locations, the next moving direction of UEs is estimated with the cosine function in order to determine the candidate E-UTRAN NodeBs (eNBs) for handover. Then, a target eNB is selected from the candidate eNBs through an angle-based dynamic weight adjustment scheme. By selecting a proper target eNB for handover, thus the quality of network transmission can be enhanced. Simulation results demonstrate the ability of the proposed scheme in reducing 17% average handover times, compared with the standard handover procedure, thereby reducing 12% average number of packet loss and 5% average packet delay time.

Efficient Content Delivery for Mobile Communications in Converged Networks

“…A mobility direction prediction mechanism was proposed in [24] for reducing the number of handovers and data losses in the LTE network. However the scope of this work is very limited due to the TCP/IP-based communication nature.…”

“…It is considered that the 120 ∘ angle is the acceptable angle towards a eNodeB as in [24]. So it is considered as an offset of = ±60 ∘ to normalize the value as shown in the following formula = −…”

“…This section presents simulation results in order to demonstrate that the content-centric LTE network is well suited to today's communication trend. This section also analyzes the performance of the content-centric LTE network with our proposed mobility management scheme and compares it with the prediction-based LTE [24] and mobility management for CCN [21]. The simulations were performed using CCNx, LENA/NS-3, Direct Code Execution (DCE) on VMware, and Ubuntu 12.04 environment.…”

Handover Algorithms in LTE Networks for Massive Means of Transport

“…They seek to improve the handover process (Chen, Kim, & Yoo, 2014;Chang, Wang, Hu, & Kao, 2013;Lee, Cho, & Kim, 2005). Likewise, Chen et al, (2014); Chang et al, (2013) and Wang, Kao, Hsiao, and Chang (2014) propose a handover technique based on the tracking of the latest UE positions using GPS. By having the previous positions -P1 and P2-and the current position (P3), the technique tries to determine the future position and the corresponding candidate cell for the handover, achieving a reduction in the number of unnecessary handovers.…”

Quick Handover in 5G for High Speed Railways and Highways Using Forward Handover and PN Sequence Detection

“…Suitable target eNodeBs are determined by estimating the UE direction with a cosine function. Thus, reducing unnecessary handovers [20], [21]. The authors of [22] present a selfoptimization mechanism as part of the Self-Organizing Network (SON) based on cell ID information, such as the number of handovers performed by the UE.…”