A Neural Network Demand Prediction Scheme for Resource Allocation in Cellular Wireless Systems

Sandhir, P.; Mitchell, K. J.

doi:10.1109/tpsd.2008.4562719

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…In the context of cellular networks, supervised learning can be applied in several domains, such as: mobility prediction [27]- [30], resource allocation [31]- [33], load balancing [34], HO optimization [35], [36], fault classification [37], [38] and cell outage management [39]- [42] Supervised learning is a very broad domain and has several learning algorithms, each with their own specifications and applications. In the following, the most common algorithms applied in the context of cellular networks are presented.…”

Section: A Supervised Learningmentioning

confidence: 99%

“…In the context of cellular systems, NNs are applied specially in the self-optimization and self-healing scenarios, in terms of resource optimization [31]- [33], [55]- [58], mobility management [27], [28], [44], [59]- [63], HO optimization [35], [36], [64], [65], and cell outage management [41]. For more information about neural networks, how they work, basic properties and learning methods readers should go to [26], [53], [66].…”

Section: A Supervised Learningmentioning

confidence: 99%

“…Some solutions, like the ones proposed in [31]- [33], [55]- [58] rely on the use of NNs in order to optimize network resources. In [31], Sandhir and Mitchell develop a scheme that predicts a cell demand after every 10 measurements taken by the system.…”

Section: G Resource Optimizationmentioning

confidence: 99%

“…In [31], Sandhir and Mitchell develop a scheme that predicts a cell demand after every 10 measurements taken by the system. At each prediction interval, the predicted resource usage in each cell is compared with the number of free channels available and channels are reallocated between cells, with the ones having more channels giving to the ones having less channels.…”

Section: G Resource Optimizationmentioning

confidence: 99%

See 3 more Smart Citations

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

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

410

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: A Supervised Learningmentioning

confidence: 99%

Section: A Supervised Learningmentioning

confidence: 99%

Section: G Resource Optimizationmentioning

confidence: 99%

Section: G Resource Optimizationmentioning

confidence: 99%

See 2 more Smart Citations

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

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

410

200

View full text Add to dashboard Cite

show abstract

“…Daroczy et al [10] used ML to predict Radio Access Bearer (RAB) sessions drops well before the end of the session. Other important work for SON in cellular networks using DNN include resource optimization [11], and mobility management [12]. Recently, Chen et al [13] combined adversarial training with variational autoencoders to unsupervised learning the behavior of abnormal KPI on the Internet.…”

Section: Related Work a Sonmentioning

confidence: 99%

Adversarial ML Attack on Self Organizing Cellular Networks

Farooq¹,

Usama²,

Qadir³

et al. 2019

Preprint

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Deep Neural Networks (DNN) have been widely adopted in self-organizing networks (SON) for automating different networking tasks. Recently, it has been shown that DNN lack robustness against adversarial examples where an adversary can fool the DNN model into incorrect classification by introducing a small imperceptible perturbation to the original example. SON is expected to use DNN for multiple fundamental cellular tasks and many DNN-based solutions for performing SON tasks have been proposed in the literature have not been tested against adversarial examples. In this paper, we have tested and explained the robustness of SON against adversarial example and investigated the performance of an important SON use case in the face of adversarial attacks. We have also generated explanations of incorrect classifications by utilizing an explainable artificial intelligence (AI) technique.

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5G Development: Automation and the Role of Artificial Intelligence

2020

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5G is expected to be commercially rolled out by 2020 and targets challenging KPI requirements. Due to the increased complexity of the mobile networks, meeting the requirements by the existing manual network operation techniques is infeasible for 5G. Also, the manual daily network operations escalate mobile networks Operation Expenses (OPEX). Without a dramatic increase in OPEX, dealing with the network complexity and meeting the KPIs for each challenging use case is only possible by the realization of autonomous network operation functions. The autonomous networks' components and the intelligent functions are developed with the cutting edge Machine Learning (ML) and Artificial Intelligence (AI) models. Furthermore, to be able to realize the efficient and scalable network automation capabilities, ONAP (Open Network Automation Platform) and OSM (Open Source Management and orchestration) frameworks are proposed for 5G. These frameworks provide a set of plug and play automation functions as well as APIs, which enables the mobile operators to extend the framework capabilities by developing their network automation functions to tackle their individual business and operational challenges. The automated network management and orchestration function, in turn, enables the operators to keep the OPEX to reasonable levels and meet the challenging KPI requirements of 5G. This article as a baseline provides an overview of 4G Long‐Term Evolution (LTE) Self‐Organizing Networks (SON) and presents 5G automation concepts and frameworks. Furthermore, in this article, the prominent 5G automation frameworks namely ONAP and OSM are compared, and evidently, the ONAP framework has enriched network automation functions and stands out as the choice of the majority of the telecommunication industry. Finally, this article summarizes the cutting edges AI models and provides a literature review on AI applications in the scope of mobile network automation.

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A Neural Network Demand Prediction Scheme for Resource Allocation in Cellular Wireless Systems

Cited by 9 publications

References 12 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

Adversarial ML Attack on Self Organizing Cellular Networks

5G Development: Automation and the Role of Artificial Intelligence

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