Enhancing indoor positioning based on partitioning cascade machine learning models

Premchaisawatt, Shutchon; Ruangchaijatupon, Nararat

doi:10.1109/ecticon.2014.6839831

Cited by 13 publications

(9 citation statements)

References 11 publications

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“…In SON scenarios, tree algorithms are basically used to perform self-optimization and healing, either by performing mobility optimization [60], coordinating SON functions [73], detecting cell outage [40] or by doing classification of Radio Link Failures (RLFs) [74]. Figure 6 shows an example of a classification decision tree adapted from [74].…”

Section: A Supervised Learningmentioning

confidence: 99%

“…For more information on K-means, please refer to [26]. In SON, K-means can be found in mobility optimization [60], caching problems [82], resource optimization [56], [88], fault detection [108], and cell outage management [103].…”

Section: B Unsupervised Learningmentioning

confidence: 99%

“…In [60], for example, the authors develop a method consisting of two cascaded ML models. The first model performs clustering via K-means while the second does classification.…”

Section: Mobility Managementmentioning

confidence: 99%

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

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

416

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: B Unsupervised Learningmentioning

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

416

200

View full text Add to dashboard Cite

show abstract

“…Few works in [64] [65] have leveraged neural networks for mobility prediction. The basic idea is to utilize the neural network to learn mobility-based model for every user and then make prediction about the future serving cell.…”

Section: B) Neural Networkmentioning

confidence: 99%

“…The basic idea is to utilize the neural network to learn mobility-based model for every user and then make prediction about the future serving cell. Authors in [64] performed clustering of the input RSS samples through kmeans. The clusters and input RSS samples were then fed to a classifying model, where neural network was used to predict the user position.…”

Section: B) Neural Networkmentioning

confidence: 99%

Mobility Management in Emerging Ultra-Dense Cellular Networks: A Survey, Outlook, and Future Research Directions

et al. 2020

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The exponential rise in mobile traffic originating from mobile devices highlights the need for making mobility management in future networks even more efficient and seamless than ever before. Ultra-Dense Cellular Network vision consisting of cells of varying sizes with conventional and mmWave bands is being perceived as the panacea for the eminent capacity crunch. However, mobility challenges in an ultradense heterogeneous network with motley of high frequency and mmWave band cells will be unprecedented due to plurality of handover instances, and the resulting signaling overhead and data interruptions for miscellany of devices. Similarly, issues like user tracking and cell discovery for mmWave with narrow beams need to be addressed before the ambitious gains of emerging mobile networks can be realized. Mobility challenges are further highlighted when considering the 5G deliverables of multi-Gbps wireless connectivity, <1ms latency and support for devices moving at maximum speed of 500km/h, to name a few. Despite its significance, few mobility surveys exist with the majority focused on adhoc networks. This paper is the first to provide a comprehensive survey on the panorama of mobility challenges in the emerging ultra-dense mobile networks. We not only present a detailed tutorial on 5G mobility approaches and highlight key mobility risks of legacy networks, but also review key findings from recent studies and highlight the technical challenges and potential opportunities related to mobility from the perspective of emerging ultra-dense cellular networks.

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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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Enhancing indoor positioning based on partitioning cascade machine learning models

Cited by 13 publications

References 11 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

Mobility Management in Emerging Ultra-Dense Cellular Networks: A Survey, Outlook, and Future Research Directions

5G Development: Automation and the Role of Artificial Intelligence

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