Cognitive self-healing system for future mobile networks

Chernogorov, Fedor; Repo, Ilmari; Räisänen, Vilho; Nihtilä, Timo; Kurjenniemi, Janne

doi:10.1109/iwcmc.2015.7289156

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…There has already been a lot of work done on the use of machine learning approaches in the cellular network domain [54][55][56][57][58][59]. In this section, we describe how SON and deep learning could play an important roles in jointly driving future cellular networks [60].…”

Section: Enabling 5g With Son and Deep Learningmentioning

confidence: 99%

Assessment of Deep Learning Methodology for Self-Organizing 5G Networks

et al. 2019

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In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of autoencoders (AE) for cell outage detection. First, we briefly introduce deep learning (DL) and also shed light on why it is a promising technique to make self organizing networks intelligent, cognitive, and intuitive so that they behave as fully self-configured, self-optimized, and self-healed cellular networks. The concept of SON is then explained with applications of intrusion detection and mobility load balancing. Our empirical study presents a framework for cell outage detection based on an autoencoder using simulated data obtained from a SON simulator. Finally, we provide a comparative analysis of the proposed framework with the existing frameworks.

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Section: Enabling 5g With Son and Deep Learningmentioning

confidence: 99%

Assessment of Deep Learning Methodology for Self-Organizing 5G Networks

et al. 2019

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“…Studies using non-parametric techniques [9] ZSIIA2015, [10] XPMZ2014, [11] WPP2016, [12] XZLLP2014 [13] OZMIGID2015 and [14] CCBR2015 [15] typically use either a variation of the k Nearest Neighbours technique or a similar approach, in which a measure of the distance, or dissimilarity, between neighbouring data items is used to compare an incoming data item with its nearest neighbours in the training set, which have already been labelled as normal or anomalous.…”

Section: Related Workmentioning

confidence: 99%

Cell Coverage Degradation Detection Using Deep Learning Techniques

Mulvey

Foh

Imran

et al. 2018

2018 International Conference on Information and Communication Technology Convergence (ICTC)

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This article presents the results of an investigation of the application of deep learning techniques to the sleeping cell problem, in order to achieve greater detection sensitivity than previously reported. We use a deep recurrent Neural Network (rNN) to process simulated RSRP reports in order to detect degradations of cell radio performance as well as complete outages. Using such a configuration we are able to achieve improved sensivity in relation to a traditional Support Vector Machine (SVM) approach, while eliminating the need for a separate dimensionality reduction stage at the front end. We study multiple rNN configurations with up to three hidden layers and conclude that in this scenario we can achieve the target improvement in sensitivity with a single hidden layer, leading to highly efficient run time performance.

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“…Automated adaptation is the next step beyond the fixed model. An approach utilizing Case-Based Reasoning (CBR) has been utilized [33,7]. Interpolation across cases is an enhancement, but is nevertheless limited by the case base.…”

Section: Approachmentioning

confidence: 99%

“…The RDF graph is then uploaded into a SPARQL server based on Fuseki 1 (6). The server dynamically updates facets from the ontology with SPARQL update scripts (7). The user can monitor the system via the GUI that interacts with the SPARQL endpoint in order to retrieve network-and MLN-related data from the ontology (8) and to update MLN formulae (9).…”

Section: Scenariomentioning

confidence: 99%

Combining ontological modelling and probabilistic reasoning for network management

Apajalahti

Hyvönen

Niiranen

et al. 2017

AIS

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Advanced automation is needed in future mobile networks to provide adequate service quality economically and with high reliability. In this paper, a system is presented that takes into account the network context, analyses uncertain information, and infers network configurations by means of probabilistic reasoning. The system introduced in this paper is an experimental platform integrating a mobile network simulator, a Markov Logic Network (MLN) model, and an OWL 2 ontology into a runtime environment that can be monitored via a Resource Description Framework (RDF)-based user interface. In this approach, the OWL ontology contains a semantic representation of the relevant concepts, and the MLN model evaluates elements of uncertain information. Experiments based on a prototype implementation demonstrate the value of semantic modelling and probabilistic reasoning in network status characterization, optimization, and visualization.

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Cognitive self-healing system for future mobile networks

Cited by 8 publications

References 14 publications

Assessment of Deep Learning Methodology for Self-Organizing 5G Networks

Assessment of Deep Learning Methodology for Self-Organizing 5G Networks

Cell Coverage Degradation Detection Using Deep Learning Techniques

Combining ontological modelling and probabilistic reasoning for network management

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