Optimization of handover algorithms in 3GPP long term evolution system

Lin, Cheng‐Chung; Sandrasegaran, Kumbesan; Ramli, Huda Adibah Mohd; Basukala, Riyaj; Patachaianand, R.; Chen, Lu; Afrin, Toyoba Sohana

doi:10.1109/icmsao.2011.5775458

Cited by 18 publications

(9 citation statements)

References 6 publications

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“…The handover decision is then made according to the triggering condition between the filtered RSRP differences and the triggering threshold. There are 3 parts in this algorithm, RSRP difference calculation, filtered RSRP difference computation, and handover decision (Zheng and Wigard, 2008;Lin et al, 2011a). The RSRP difference calculation is presented in equation (5) DIFs _ j t = RSRP t − RSRP $ t…”

Section: Methodsmentioning

confidence: 99%

“…In lieu of this, optimizing the handover procedure to get the required performance is considered as one important issue in mobile networks (Hämäläinen, 2011). LTE Handover is a process that transfers a UE from one evolved NodeB (eNodeB) to another eNodeB or one sector to another sector within the same eNodeB due to perceived better cell coverage from the target eNodeB (Lin et al, 2011a). This goal is achieved by analyzing a periodic or event triggered downlink received signal strength (RSS) and carrier-tointerference ratio (CIR) measurements from the UEs.…”

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confidence: 99%

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Evaluating the effect of mobility speed on the performance of three handover algorithms in long term evolution networks

Osifeko¹,

Okubanjo²,

Abolade³

et al. 2018

Journal of Applied Sciences and Environmental Management

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This work uses NS3 simulation to study the effect of mobility speed on the performance of three handover algorithms in Long Term Evolution (LTE) Networks. A realistic multi-cell LTE network was set up using NS3 simulation software. Mobility models were used to vary the location of the User Equipment (UE), hence triggering handover events across the network. The performance was measured using Signal Interference Noise Ratio (SINR) and number of completed handovers. Result revealed that at a speed between the ranges of 0 -3 km/h, the Integrative algorithm performed best while at 4 -60km/h, the performance of the A3RSRP algorithm was the best with an average value of 95dB. Also, at an increased speed within the range of 60 -120 km/h, the Integrative algorithm had a slightly better performance than the A3RSRP. However, at a speed above 120 km/h, the integrative algorithm performed best with an SINR of 120dB. In terms of completed handovers, the Integrative algorithm had the least number of completed handovers throughout the entire range of considered speeds. Thus, we establish that mobility speed has a significant effect on the performance of handover algorithms. The Long-Term Evolution (LTE) system was designed with the aim of providing a higher data rates and lower latency under various mobility conditions (Dimou et al., 2009). According to (3GPP TR 25.913), the LTE system is expected to provide mobility support for User Equipment (UE) up to speeds of 500 km/h while maintaining an uninterrupted provision of high data rates and services. Mobility at high speed has always been a challenge in wireless networks and LTE was designed to overcome this challenge. To accomplish this purpose, LTE must minimize delay and packet loss in voice transmission and ensure reliability in data transmission during high-speed scenarios. In lieu of this, optimizing the handover procedure to get the required performance is considered as one important issue in mobile networks (Hämäläinen, 2011). LTE Handover is a process that transfers a UE from one evolved NodeB (eNodeB) to another eNodeB or one sector to another sector within the same eNodeB due to perceived better cell coverage from the target eNodeB (Lin et al, 2011a). This goal is achieved by analyzing a periodic or event triggered downlink received signal strength (RSS) and carrier-tointerference ratio (CIR) measurements from the UEs. The eNodeB then decides based on the received parameters on whether to handover the UE to the neighboring eNodeB or keep the UE connected to it. The decision-making process is controlled by an efficient handover algorithm as it enhances the system capacity and the service quality cost effectiveness. The performance of the LTE handover scheme depends majorly on the handover algorithm in use (Hans et al, 2014). Due to this fact, researchers have channeled efforts at optimizing existing algorithms while some new ones have been developed. Three of the numerous algorithms that have become popular in LTE networks include (i) Power Budget Handover ...

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Evaluating the effect of mobility speed on the performance of three handover algorithms in long term evolution networks

Osifeko¹,

Okubanjo²,

Abolade³

et al. 2018

Journal of Applied Sciences and Environmental Management

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show abstract

“…A novel approach to handover management for LTE femtocells is presented in [8], which runs on the femtocell base station, does not require any prior knowledge of the architecture of the building in which it is deployed; thus it is fully consistent with the self-organizing network plug-n-play requirement. Three well known handover algorithms have been optimized in the LTE system [9]. And the simulation results show that this optimization outperforms non-optimized algorithms by minimizing the average number of handover.…”

Section: Introductionmentioning

confidence: 92%

Handover Optimization Algorithm in LTE High-Speed Railway Environment

Fang

Deng

Jiang

et al. 2015

Wireless Pers Commun

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The traditional A3 event-based HO algorithms are mainly designed for the low speed (\30 m/s) networks. That aren't suitable for the high-speed railway scenario which the link quality may deteriorate sharply and the wireless channel environment may become unstable with the increase of velocity. To overcome the disadvantages of the handover algorithms in LTE high-speed railway networks, we proposed a handover optimization algorithm based on statistics, where we not only consider reference signal received power and reference signal received quality at the same time but also the rate of cell resources change. The simulation results show that the proposed algorithm has higher handover success rate and lower handover numbers. Thus unnecessary handover is reduced by up to 47 % and the novel algorithm provides success rate of 0.5-13.9 % higher than the classical A3 algorithm under different conditions of velocity, and greatly improve handover performance.

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“…In Ref. [6], the impacts of triggering setting (hysteresis/TTT) on handover performance in LTE system have been investigated for different scenarios of low, medium and high system loads. Kitagawa and Komine [7] proposed a self-optimization algorithm for handover (HO) parameters which was robust against the change in user (UE) mobility.…”

Section: Introductionmentioning

confidence: 99%

Position-assisted fast handover schemes for LTE-advanced network under high mobility scenarios

Fei

Fan

2012

J. Mod. Transport.

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Two position-assisted fast handover schemes, scheme A and scheme B, for LTE-A system under very high mobility scenarios, are proposed, together with their performance evaluation. Scheme A is designed to reduce handover delay by making handover preparation before handover starts. Scheme B aims at reducing unnecessary handovers and improving handover success rate, by calculating the geographically best target handover cell, which makes it easier for mobile terminals to access the target cell. A system level simulation is conducted to evaluate the performance of these two schemes. It is shown that, scheme A could reduce inter-site handover delay by about 50 ms, while scheme B could cut down nearly 50% of all handovers when time-to-trigger (TTT) is 0 ms. Besides, as TTT gets larger, Scheme B has much better success rate.

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Optimization of handover algorithms in 3GPP long term evolution system

Cited by 18 publications

References 6 publications

Evaluating the effect of mobility speed on the performance of three handover algorithms in long term evolution networks

Evaluating the effect of mobility speed on the performance of three handover algorithms in long term evolution networks

Handover Optimization Algorithm in LTE High-Speed Railway Environment

Position-assisted fast handover schemes for LTE-advanced network under high mobility scenarios

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