A self‐organized resource allocation scheme for heterogeneous macro‐femto networks

Mehta, Mahima; Rane, Nirbhay; Karandikar, Abhay; Imran, Muhammad Ali; Evans, B.G.

doi:10.1002/wcm.2518

Cited by 11 publications

(8 citation statements)

References 23 publications

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“…These controllers can be found in all domains of SON, and its applications include but are not limited to: NCL configuration [118]- [120], radio parameters configuration [121]- [123], coverage and capacity optimization [122]- [130], HO optimization [131]- [144], load balancing [145], [146], resource optimization [123], [147]- [149], coordination of SON functions [150]- [152], fault detection [153] and cell outage management [154]- [161].…”

Section: Controllersmentioning

confidence: 99%

“…Mehta et al, in [130], develop two solutions in order to address the problem of co-layer interference (interference between neighbors) in a heterogeneous macrocell and femtocell network scenario. The two schemes attempt to mitigate colayer interference while also improving the minimum data rate achieved by femtocell users and ensuring fairness to them.…”

Section: Coverage and Capacitymentioning

confidence: 99%

See 1 more Smart Citation

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

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

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417

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: Controllersmentioning

confidence: 99%

Section: Coverage and Capacitymentioning

confidence: 99%

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

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

Self Cite

417

200

View full text Add to dashboard Cite

show abstract

“…green communication technique) when the traffic level is below threshold [84], [47]. When the macro BS is congested, then by mobile traffic offloading to femto cell either by co-operative game theory [46] or by markov`s queuing theory [82] the improvement of EE of the Macro BS and SE of the small BS can be obtained [48], [49][50][51][52][53]. By using Nash equilibrium game theory a trade-off relationship between the SE and EE efficiency can be established [44], [45].…”

Section: IImentioning

confidence: 99%

Coverage Probability of Hetnet Using Stochastic Geometry and Poisson Point Process

Sahu¹,

Pawar²

2018

IJCA

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The exponential growth in the number of cellular users along with their increasing demand of higher transmission rate, ultra low latency, guaranteed QoS and lower power consumption is of prime importance for the design of future generation networks. The spectral efficiency (SE) can be improved by better utilization of the network resources at the cost of reduction in the energy efficiency (EE) due to increase in the network power expenditure arising from densification of the network. One of the possible solution is to deploy Heterogeneous Network (HetNet) consisting of several tiers of small cell BSs overlaid within the coverage area of the macrocell. The HetNet can provide better coverage and data rate to the cell edge users in comparison to the macrocell only deployment. Therefore, optimal network planning is an important challenge for future heterogeneous network with high number of small cells. In order to tackle these challenges, here we focus on the random spatial modeling of Multi Tier Heterogeneous Network consisting of dense small cell access points using Poisson Point Process (PPP) with Vornoi Tessellation. Then we calculate the coverage and outage probability of the cellular network by using stochastic geometry.

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“…Örneğin, indirme yolu yönünde çeşitli güç yönetim teknikleri [13] çalışmasında verilmiştir. Ortak katman girişiminin azaltılması için öz-örgütlü bir güç kontrol yöntemi [7] çalışmasında önerilmiştir. Sonuçlar heterojen ağda kapasite kullanımının arttığını göstermektedir.…”

Section: Introductionunclassified

Power management optimization for femtocells in heterogeneous LTE networks

Akbulut¹,

Kalaycıoğlu²

2019

Pamukkale J Eng Sci

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Bu çalışmada makro hücrelerin yanında çok daha düşük iletim gücüne sahip femto hücrelerin bulunduğu bir heterojen LTE ağında genetik algoritma eniyileme yöntemi kullanılarak femto hücreler için bir güç yönetimi önerilmektedir. Elde edilen benzetim sonuçları femto hücreler için güç kontrolünün indirme yolu yönünde girişimin bozucu etkilerinin azaltılmasında önemli rol oynadığını göstermektedir. Benzetimlerde femto hücre baz istasyonlarının iletim gücü sabit tutulduğundaki durum ile eniyileme yapıldığındaki sonuçlar kıyaslanmıştır. Eniyileme sonucunda, femto hücre kullanıcılarının sahip olduğu sinyal-girişim ve gürültü oranında hissedilir bir başarım kaybı olmamaktadır. Buna karşın önerilen güç yönetimi sayesinde femto hücre baz istasyonlarının iletim gücünde %50'ye yakın azalma sağlanabilmektedir. In this study, a power management scheme using genetic algorithm optimization method in a heterogeneous LTE network in which there exists femtocells having a low transmission power among macrocells is proposed. The simulation results show that power management for femtocells in the downlink direction plays an important role in reducing the degrading effects of the interference. In the simulations, the results of keeping fixed transmission power of femtocell base stations situation are compared with that of performing optimization case. There is no observable performance loss in the femtocell user's signal-interference and noise ratio due to the power management optimization. On the contrary, almost 50% transmission power decrease of femtocell base station can be provided by means of the proposed power management scheme.

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A self‐organized resource allocation scheme for heterogeneous macro‐femto networks

Cited by 11 publications

References 23 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

Coverage Probability of Hetnet Using Stochastic Geometry and Poisson Point Process

Power management optimization for femtocells in heterogeneous LTE networks

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