Hysteresis Margin and Load Balancing for Handover in Heterogeneous Network

Ray, Ranada Prasad; Tang, Lun

doi:10.7763/ijfcc.2015.v4.391

Cited by 27 publications

(21 citation statements)

References 5 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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“…The work in [13] optimized HCPs on the basis of enhanced mobility state estimation, which considers two parameters, namely, UE velocity and HO types, to select the optimal TTT value. Meanwhile, the authors in [12] introduced an adaptive algorithm that selects different values of HOM and load balancing for each UE in the HetNet. HO decision in the proposed algorithm utilizes the signal-tointerference-plus-noise ratio (SINR) instead of the received signal strength indicator to calculate the actual level of HOM.…”

Section: Related Workmentioning

confidence: 99%

“…It focuses on the threshold of measurement reports without periodically adjusting HCPs in accordance with user experience. Several studies, such as [3], [12]- [14] have proposed various MRO algorithms to reduce HOF by improving HO performance in HetNets. Although these algorithms slightly improve HO performance, interesting and relevant HO problems still need to be addressed, particularly when these algorithms are implemented for small cells.…”

Section: Introductionmentioning

confidence: 99%

Auto Tuning Self-Optimization Algorithm for Mobility Management in LTE-A and 5G HetNets

et al. 2020

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Ultra-dense networks represent the trend for future wireless 5G networks, which can provide high transmission rates in dense urban environments. However, a massive number of small cells are required to be deployed in such networks, and this requirement increases interference and number of handovers (HOs) in heterogeneous networks (HetNets). In such scenario, mobility management becomes an important issue to guarantee seamless communication while the user moves among cells. In this paper, we propose an autotuning optimization (ATO) algorithm that utilizes user speed and received signal reference power to adapt HO margin and time to trigger. The proposed algorithm aims to reduce the number of frequent HOs and HO failure (HOF) ratio. The performance of the proposed algorithm is evaluated through simulation with a two-tier model that consists of 4G and 5G networks. Simulation results show that the average rates of pingpong HOs and HOF are significantly reduced by the proposed algorithm compared with other algorithms from the literature. In addition, the ATO algorithm achieves a low call drop rate and reduces HO delay and interruption time during user mobility in HetNets. INDEX TERMS Ultra-dense, heterogeneous networks, handover, self-optimization.

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“…To analyze the performance of the proposed algorithm, simulations are performed under different mobile speeds. The proposed algorithm is compared with a dynamic algorithm and with those proposed by Ray et al [11] and Nie et al [9] algorithms. Figure 6 depicts the average HOP of all algorithms under different UE speeds.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…However, the performance with respect to other HO performance metrics, such as HOPP, RLF, and delay, have not been discussed. Researchers [11] have also introduced an adaptive algorithm that selects different values of HOM and load balancing for each UE in HetNets. The HO decision in the proposed algorithm depends on the signal-to-interference-plus-noise-ratio (SINR) rather than on the received signal strength indicator, which is then used to calculate the actual level of the HOM.…”

Section: Introductionmentioning

confidence: 99%

Velocity-Aware Handover Self-Optimization Management for Next Generation Networks

et al. 2020

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The fifth generation (5G) network is an upcoming standard for wireless communications that coexists with the current 4G network to increase the throughput. The deployment of ultra-dense small cells (UDSC) over a macro-cell layer yields multi-tier networks, which are known as heterogeneous networks (HetNets). HetNets play a key role in the cellular network to provide services to numerous users. However, the number of handovers (HOs) and radio link failure (RLF) greatly increase due to the increase in the UDSC in the network. Therefore, mobility management becomes a very important function in a self-organizing network to improve the system performance. In this paper, we propose a velocity-based self-optimization algorithm to adjust the HO control parameters in 4G/5G networks. The proposed algorithm utilizes the user's received power and speed to adjust the HO margin and the time to trigger during the user's mobility in the network. Simulation results demonstrate that the proposed algorithm achieves a remarkable reduction in the rate of ping-pong HOs and RLF compared with other existing algorithms, thereby outperforming such algorithms by an average of more than 70% for all HO performance metrics.

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Hysteresis Margin and Load Balancing for Handover in Heterogeneous Network

Cited by 27 publications

References 5 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

Auto Tuning Self-Optimization Algorithm for Mobility Management in LTE-A and 5G HetNets

Velocity-Aware Handover Self-Optimization Management for Next Generation Networks

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