On the Potential of Handover Parameter Optimization for Self-Organizing Networks

Muñoz, Pablo; Barco, Raquel; Bandera, Isabel de la

doi:10.1109/tvt.2013.2247778

Cited by 98 publications

(83 citation statements)

References 14 publications

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“…FLCs are also widely used in the context of HO optimization, as it can be seen in the works of [7], [165]- [172]. All of these algorithms consists of gathering certain network related metrics, fuzzifying them and making decisions in order to optimize HO margins, thresholds, hysteresis, TTT, or other attributes, so that the network can make better HO decisions.…”

Section: E Handover Parameters Optimizationmentioning

confidence: 99%

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

412

200

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Abstract-In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future.

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Section: E Handover Parameters Optimizationmentioning

confidence: 99%

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

412

200

View full text Add to dashboard Cite

show abstract

“…In this paper, we follow the real scenario and will not assign resources when there are not enough PRBs for new coming active UEs [6], [7].…”

Section: Load Measurementmentioning

confidence: 99%

Hysteresis Margin and Load Balancing for Handover in Heterogeneous Network

Ray¹,

Tang²

2015

IJFCC

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Abstract-Long term evolution (LTE) heterogeneous network can represent improvement of cell coverage and network capacity by adopting advanced physical layer techniques. In heterogeneous network, one of the major challenges is the handover decision between different eNBs. The utilization of the picocells results to use more network resources and handover procedure. In this paper, we investigate the hysteresis margin and load balancing problem in a 3GPP LTE heterogeneous network. Firstly, we consider A3 event, Hysteresis Margin (HM) and Cell Individual Offset (CIO) objectives are not fixed for every cells. Then we analyze the complexity of the problem and propose a practical algorithm which calculate adaptive hysteresis margin and load balancing in heterogeneous network. The results show that our algorithm can lead to significantly better performances, such as redundant handovers reduction and improvement of the network performance.Index Terms-LTE heterogeneous network, hysteresis margin, cell individual offset, load balancing.

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“…Mobility management techniques for HetNets have been recently investigated in the literature, e.g., [23][24][25][26][27][28]. In [23], the authors evaluate the effect of different combinations of various MM parameter settings for HetNets.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The simulation results in [23] consider that all UE have the same velocity in each simulation setup. Further results on the effects of MM parameters are presented in [24], where the authors propose a fuzzy-logic-based controller. This controller adaptively modifies handover parameters for handover optimization by considering the system load and UE speed in a macrocell-only network.…”

Section: Literature Reviewmentioning

confidence: 99%

Mobility management in HetNets: a learning-based perspective

Simsek

Bennis

Güvenç

2015

J Wireless Com Network

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Heterogeneous networks (HetNets) are expected to be a key feature of long-term evolution (LTE)-advanced networks and beyond and are essential for providing ubiquitous broadband user throughput. However, due to different coverage ranges of base stations (BSs) in HetNets, the handover performance of a user equipment (UE) may be significantly degraded, especially in scenarios where high-velocity UE traverse through small cells. In this article, we propose a context-aware mobility management (MM) procedure for small cell networks, which uses reinforcement learning techniques and inter-cell coordination for improving the handover and throughput performance of UE. In particular, the BSs jointly learn their long-term traffic loads and optimal cell range expansion and schedule their UE based on their velocities and historical data rates that are exchanged among the tiers. The proposed approach is shown not only to outperform the classical MM in terms of throughput but also to enable better fairness. Using the proposed learning-based MM approaches, the UE throughput is shown to improve by 80% on the average, while the handover failure probability is shown to reduce up to a factor of three.

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On the Potential of Handover Parameter Optimization for Self-Organizing Networks

Cited by 98 publications

References 14 publications

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Hysteresis Margin and Load Balancing for Handover in Heterogeneous Network

Mobility management in HetNets: a learning-based perspective

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