Effects of time-to-trigger parameter on handover performance in SON-based LTE systems

Lee, Yejee; Shin, Bongjhin; Lim, Jaechan; Hong, Daehyoung

doi:10.1109/apcc.2010.5680001

Cited by 35 publications

(8 citation statements)

References 6 publications

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“…One of the main issues related to mobility is the optimization operation for HCPs. Several algorithms have been developed to automatically perform self-optimization for HCPs [56,[111][112][113][114][115][116][117][118][119][120][121][122][123]. To the best of our knowledge, the most available self-optimization algorithms were designed based on the concept of central control and optimization for all systems.…”

Section: Central Self-optimization Operationmentioning

confidence: 99%

Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

et al. 2020

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Ensuring a seamless connection during the mobility of various User Equipment (UE) will be one of the major challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. Several key determinants will significantly contribute to numerous mobility challenges. One of the most important determinants is the use of millimeter waves (mm-waves) as it is characterized by high path loss. The inclusion of various types of small coverage Base Stations (BSs), such as Picocell, Femtocell and drone-based BSs is another challenge. Other issues include the use of Dual Connectivity (DC), Carrier Aggregation (CA), the massive growth of mobiles connections, network diversity, the emergence of connected drones (as BS or UE), ultra-dense network, inefficient optimization processes, central optimization operation, partial optimization, complex relation in optimization operations, and the use of inefficient handover decision algorithms. The relationship between these processes and diverse wireless technologies can cause growing concerns in relation to handover associated with mobility. The risk becomes critical with high mobility speed scenarios. Therefore, mobility issues and their determinants must be efficiently addressed. This paper aims to provide an overview of mobility management in 5G networks. The work examines key factors that will significantly contribute to the increase of mobility issues. Furthermore, the innovative, advanced, efficient, and smart handover techniques that have been introduced in 5G networks are discussed. The study also highlights the main challenges facing UEs' mobility as well as future research directions on mobility management in 5G networks and beyond.

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Section: Central Self-optimization Operationmentioning

confidence: 99%

Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

et al. 2020

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“…A secondary objective of MRO was to avoid the ping pong effect. Many studies, such as [18][19][20][21][22], have been conducted to fulfill the MRO objectives. The authors in [18][19][20][21] proposed adjusting only HOM without considering the impact of TTT, but this may cause an increase in HOPP.…”

Section: Related Workmentioning

confidence: 99%

“…Many studies, such as [18][19][20][21][22], have been conducted to fulfill the MRO objectives. The authors in [18][19][20][21] proposed adjusting only HOM without considering the impact of TTT, but this may cause an increase in HOPP. In contrast, the authors in [23] proposed tuning only TTT and ignoring the effect of HOM, but this may result in increased HO delay, which leads to increased HOF.…”

Section: Related Workmentioning

confidence: 99%

Handover in LTE Networks with Proactive Multiple Preparation Approach and Adaptive Parameters Using Fuzzy Logic Control

2015

KSII TIIS

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High data rates in long-term evolution (LTE) networks can affect the mobility of networks and their performance. The speed and motion of user equipment (UE) can compromise seamless connectivity. However, a proper handover (HO) decision can maintain quality of service (QoS) and increase system throughput. While this may lead to an increase in complexity and operational costs, self-optimization can enhance network performance by improving resource utilization and user experience and by reducing operational and capital expenditure. In this study, we propose the self-optimization of HO parameters based on fuzzy logic control (FLC) and multiple preparation (MP), which we name FuzAMP. Fuzzy logic control can be used to control self-optimized HO parameters, such as the HO margin and time-to-trigger (TTT) based on multiple criteria, viz HO ping pong (HOPP), HO failure (HOF) and UE speeds. A MP approach is adopted to overcome the hard HO (HHO) drawbacks, such as the large delay and unreliable procedures caused by the break-before-make process. The results of this study show that the proposed method significantly reduces HOF, HOPP, and packet loss ratio (PLR) at various UE speeds compared to the HHO and the enhanced weighted performance HO parameter optimization (EWPHPO) algorithms.

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“…The algorithm neglects services that are not in real-time. Lee et al [11] provided a HO mechanism to analyze the ping-pong handover, the latency of handover, and the failure of radio links for each handover when the UE speed is in the range of 3, 15, 30, 60, and 120 km/h. The UE speed has a direct negative effect on radio link failure and ping-pong; however, radio link failure remains stable when the time to trigger is 5120 ms. Xu et al [12] generated an algorithm that periodically (every 100 ms) measures the reference signal received power (RSRP) and UE speed value.…”

Section: Introductionmentioning

confidence: 99%

Real-time high-speed mobility management

Abuelgasim

Hassan

Khairi

et al. 2021

IJEECS

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High-speed mobility system has now become a serious concern for mobile operators due to the large frameworks of a heterogeneous network made up of multiple cell types and different frequency bands. Handover (HO) is conducted in a real-life scenario when the user equipment (UE) moves from one network coverage to another by performing proper measurement with high speed. HO breakdown and call loss are observed due to a high speed; thus, high-speed mobility system needs improvement by using the UE speed as one of the key measurement monitoring criteria for the long-term evolution (LTE) network. Vendor consultation has been considered in this paper in addition to real drive test measurement in highways. Results have shown that velocity has a direct impact on the handover quality and overall timing. Results also demonstrate that 120 km/h measurement is better than 140 km/h as UE speed.

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Effects of time-to-trigger parameter on handover performance in SON-based LTE systems

Cited by 35 publications

References 6 publications

Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

Handover in LTE Networks with Proactive Multiple Preparation Approach and Adaptive Parameters Using Fuzzy Logic Control

Real-time high-speed mobility management

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