Distributed Self-Optimization of Handover for the Long Term Evolution

Schröder, André; Lundqvist, Henrik; Nunzi, Giorgio

doi:10.1007/978-3-540-92157-8_26

Cited by 18 publications

(7 citation statements)

References 1 publication

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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%

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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%

“…Some algorithms in the literature operate based on this concept. That is, some algorithms optimize only one HCP (the HOM), such as in [114,117,123], while the other HCPs are considered to be fixed. Utilizing fixed TTT may cause one handover issue that HPO aims to address.…”

Section: The Mesh Relationships (Conflict Problem) Between Lbo and Hpmentioning

confidence: 99%

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

et al. 2020

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“…The WPHPO algorithm is evaluated as a function of Handover Failure Probability (HFP), HPPP, and DCP. The studies in [19] and [25] proposed SON algorithms, but only one HCP was considered in their analysis. Whereas in [18] and [20], techniques in modifying both the HOM and TTT were proposed, however, the impact on the UE speed were not investigated.…”

Section: Introductionmentioning

confidence: 99%

“…Some existing algorithms do not optimize all HCPs. For example, some algorithms only optimize one HCP (the HOM), such as in [16,19,25]. This may cause an increased in handover as well.…”

Section: Introductionmentioning

confidence: 99%

Individualistic Dynamic Handover Parameter Self-Optimization Algorithm for 5G Networks Based on Automatic Weight Function

et al. 2020

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“…Similar studies in this field [3][4][5] usually attempt to minimize only one of the HO performance indicators (HPIs), such as Radio Link Failures (RLFs), HO ping-pongs (HPPs) or HO failures (HOFs), in an effort of improving network performance. However, since the relationships between these HPIs is a very close one, they should be considered as a whole when trying to optimize HO performance.…”

Section: Introductionmentioning

confidence: 99%

An enhanced weighted performance-based handover parameter optimization algorithm for LTE networks

Balan

Sas²,

Jansen

et al. 2011

J Wireless Com Network

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This article introduces an enhanced version of previously developed self-optimizing algorithm that controls the handover (HO) parameters of a long-term evolution base station in order to diminish and prevent the negative effects that can be introduced by HO (radio link failures, HO failures and ping-pong HOs) and thus improve the overall network performance. The default algorithm selects the best hysteresis and time-to-trigger combination based on the current network status. The enhancement proposed here aims to maximize the gain provided by the algorithm by improving its convergence time. The effects of this enhancement have been studied in a rural scenario setting and compared to the original algorithm; the results show a clear improvement, faster convergence, and better network performance, because of the enhancement.

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Distributed Self-Optimization of Handover for the Long Term Evolution

Cited by 18 publications

References 1 publication

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

Individualistic Dynamic Handover Parameter Self-Optimization Algorithm for 5G Networks Based on Automatic Weight Function

An enhanced weighted performance-based handover parameter optimization algorithm for LTE networks

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