Challenges and practical implementation of self-organizing networks in LTE/LTE-Advanced systems

Marwangi, M. M. S.; Fisal, Norsheila; Yusof, Sharifah Kamilah Syed; Rashid, Rozeha A.; Ghafar, Aimi Syamimi Ab; Saparudin, Faiz Asraf; Katiran, Norshidah

doi:10.1109/icimu.2011.6122748

Cited by 16 publications

(9 citation statements)

References 15 publications

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“…Current state-of-the-art algorithms go all the way from basic control loops and threshold comparisons to more complex ML and data mining techniques [14]. As the field develops, there is a significant trend of implementing more robust and advanced techniques which would in turn solve more complex problems [15]. This paper also provides a basic overview of the current state-of-the-art ML techniques that are being developed and applied to cellular networks.…”

Section: B Machine Learning In Sonmentioning

confidence: 99%

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

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

393

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: B Machine Learning In Sonmentioning

confidence: 99%

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

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

393

200

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“…Once the 4 th generation (4G) cellular network was introduced, Self Organizing Network (SON) functions started to have gained popularity, since the 4G network was complex. A SON function is said to be effective with the following four key components [6]: i) Autonomy, ii) Scalability, iii) Adaptability, and iv) Intelligent [7]. The following table 1 shows the key components and its corresponding SON functions.…”

Section: History Of Self-healingmentioning

confidence: 99%

Self-Healing and Resilience of Complex Network

ADC¹,

Sabapathi²

2019

IJRTE

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Currently mobile operators spend more money towards maintaining the mobile network in fsinding faults, which will degrade the cellular services. If any fault occurs in the network it must be healed automatically and made to work. This self-healing network gives solutions for fixing or helps in deciding the problems to be carried out autonomously with no human interference. Also the networks ability to recover after any failure is said to be resilience. This paper discusses the strategy of resilience of complex network, its principles and metrics of resilience. The main aim of self-healing and resilience is to heal the network automatically and the network must recover after any fault.

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“…A brief description of conflicts between control parameters of certain SO functions is given in [5]. The Socrates project team organised some basic inter-related parameters into functional parameter groups in [6].…”

Section: Introductionmentioning

confidence: 99%

A framework for classification of Self-Organising network conflicts and coordination algorithms

Lateef

Imran

Abu-Dayya

2013

2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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The next generation Long Term Evolution (LTE) & LTE-Advanced cellular networks will be equipped with numerous Self-Organizing (SO) functions. These SO functions are being envisioned to be inevitable for technical as well as commercial viability of LTE/LTE-Advanced networks. Therefore, a lot of research effort is currently being channeled to the design of various SO functions. However, given the convoluted and complex interrelationships among cellular system design and operational parameters, a large number of these SO functions are highly susceptible to parametric or logical inter-dependencies. These inter-dependencies can induce various types of conflicts among them, thereby undermining the smooth and optimal network operation. Therefore, an implicit or explicit self-coordination framework is essential, not only to avoid potential objective or parametric conflicts among SO functions, but also to ensure the stable operation of wireless networks. In this paper we present such a self-coordination framework. Our framework builds on the comprehensive identification and classification of potential conflicts that are possible among the major SO functions envisioned by Third Generation Partnership Project (3GPP) so far. This classification is achieved by analyzing network topology mutation, temporal and spatial scopes, parametric dependencies, and logical relations that can affect the operation of SO functions in reality. We also outline a solution approach for a conflict-free implementation of multiple SO functions in LTE/LTE-Advanced networks. Moreover, as an example, we highlight future research challenges for optimum design of Mobility Load Balancing (MLB) and Mobility Robustness Optimisation (MRO).

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Challenges and practical implementation of self-organizing networks in LTE/LTE-Advanced systems

Cited by 16 publications

References 15 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

Self-Healing and Resilience of Complex Network

A framework for classification of Self-Organising network conflicts and coordination algorithms

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