Self organizing method for handover performance optimization in LTE-advanced network

Chaudhuri, Saptarshi; Baig, Irfan; Das, Debabrata

doi:10.1016/j.comcom.2017.06.009

Cited by 27 publications

(21 citation statements)

References 5 publications

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“…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. In other studies, such as [16], the authors used the reinforcement learning concept to detect HO in a network. Effective session HOs lead to low CDR and also reduce HOF and HOPP.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

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

et al. 2020

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“…When addressing HO in LTE, existing works are usually divided into targeting the reduction of RLF [34], reduction of ping-pongs (PPs) [35], or a combination of both [36]- [38], reduction of HO rate [23], [30], [39], and reduction of HOFs [23], [40], [41]. Other works are devoted to finding the signaling overheads and the power consumption during HO [42], assess the energy-saving [13], [36], load balancing [43], the impact of inter-site distance (ISD) [26], [44], and UE speed [26] on the HO process.…”

Section: B Literature Surveymentioning

confidence: 99%

“…The techniques to reduce both, the RLFs and PPs are presented in [36]- [38]. A reduced early HO scheme was proposed in [36] to reduce the RLF and PPs and achieve high energy efficiencies, at the cost of keeping the other performance parameters within acceptable limits.…”

Section: B Literature Surveymentioning

confidence: 99%

A Survey on Handover Management: From LTE to NR

2019

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To satisfy the high data demands in future cellular networks, an ultra-densification approach is introduced to shrink the coverage of base station (BS) and improve the frequency reuse. The gain in capacity is expected but at the expense of increased interference, frequent handovers (HOs), increased HO failure (HOF) rates, increased HO delays, increase in ping pong rate, high energy consumption, increased overheads due to frequent HO, high packet losses and bad user experience mostly in high-speed user equipment (UE) scenarios. This paper presents the general concepts of radio access mobility in cellular networks with possible challenges and current research focus. In this article, we provide an overview of HO management in longterm evolution (LTE) and 5G new radio (NR) to highlight the main differences in basic HO scenarios. A detailed literature survey on radio access mobility in LTE, heterogeneous networks (HetNets) and NR is provided. In addition, this paper suggests HO management challenges and enhancing techniques with a discussion on the key points that need to be considered in formulating an efficient HO scheme. INDEX TERMS Radio access mobility, cell selection, handover, LTE, 5G, NR, mobility enhancers. Ph.D. degree in electrical engineering with Aalto University, Finland. He has more than two years, from 2013 to 2015, of professional experience as an RF Planning and Optimization Executive at Wi-Tribe Pakistan Ltd. (Ooredoo Group). During his stay at KFUPM, he was associated with the King Abdullah University of Science and Technology (KAUST), Saudi Arabia, as a Visiting Student and a KFUPM-KAUST Joint Research Initiative. He has been a Researcher with the Helsinki Research Center, Huawei Technologies Finland Oy, since February 2018. His current research interest includes energy-efficient mobility for small-cell overlaid cellular networks. He received the Gold Medal Award for obtaining the first position in the B.Sc. degree. XAVIER GELABERT received the M.Sc. degree in electrical engineering from the KTH Royal Institute of Technology, Stockholm, in 2003, and the joint B.Sc. and M.Sc. degrees in telecom engineering and the Ph.D. degree (Hons.

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“…The article [22] proposes the algorithm entitled handover detection self-organizing handover parameter (HD-SOHP) for eNodeB systems. The algorithm uses reinforcement learning (RL), and the solution uses Markov decision process (MDP) methodologies.…”

Section: Related Workmentioning

confidence: 99%

A Novel Heuristic for Handover Priority in Mobile Heterogeneous Networks

et al. 2020

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Handover (HO) is designed to facilitate user mobility and ensure quality of service in mobile networks. In multiple base station (eNodeBs) scenarios, the HO priority process is a problem that has been studied in many surveys, as neglecting the use of priority-based schemes can result in high amounts of HO and, consequently, a decrease in the quality of services provided. This paper presents a Heuristic for Handover based on AHP-TOPSIS-FUZZY (H 2 ATF), which generates a priority ranking of eNodeBs from the use of (a) the analytical hierarchical process (AHP) to define the weights of the criteria; (b) the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the selected target cells; and (c) the use of an adaptive hysteresis calculated through a fuzzy inference system based on parameters that directly impact the HO process. Through this proposal, it was possible to define the best time and, together, the best antenna to perform the HO. The results demonstrate a decrease of up to 43% in HO ping pong (HPP), a widely used metric in the literature to evaluate HO heuristics. INDEX TERMS Handover, priority, heterogeneous networks, mobile networks, AHP-TOPSIS, fuzzy logic.

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Self organizing method for handover performance optimization in LTE-advanced network

Cited by 27 publications

References 5 publications

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

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

A Survey on Handover Management: From LTE to NR

A Novel Heuristic for Handover Priority in Mobile Heterogeneous Networks

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