A Mobile Network Planning Tool Based on Data Analytics

Moysen, Jessica; Giupponi, Lorenza; Mangues‐Bafalluy, Josep

doi:10.1155/2017/6740585

Cited by 20 publications

(16 citation statements)

References 22 publications

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“…Moysen et al [142] PCA PCA is used for low dimensional feature extraction in a mobile network planning tool based on data analytic. Ossia et al [143] PCA & Simple Embedding PCA combined with simple embedding from deep learning is used for dimensionality reduction which reduces the communication overhead between client and server.…”

Section: Network Operations Optimization and Analyticsmentioning

confidence: 99%

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

et al. 2019

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While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.

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Section: Network Operations Optimization and Analyticsmentioning

confidence: 99%

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

et al. 2019

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“…However it is of interest for many problems to reduce the dimension of the original data. For example, in [110], [111], the authors face the problem of the huge amount of potential features the system has as input, and they suggest that the regression analysis has a better performance in a reduced space. In this context, the most common methods are: Feature Extraction (FE) and Feature Selection (FS) [112].…”

Section: B Unsupervised Learning (Ul)mentioning

confidence: 99%

“…By doing PCA/SPCA on the input features, and promoting solutions in which only a small number of input features capture most of the variance, the number of random variables under consideration is reduced. Based on previous results, in [111], [183] the same authors define a methodology to build a tool for smart and efficient network planning, based on QoS prediction derived by proper data analysis of UE measurements in the network.…”

Section: ) Son Conflicts Coordinationmentioning

confidence: 99%

From 4G to 5G: Self-organized network management meets machine learning

Moysen

Giupponi

2018

Computer Communications

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172

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In this paper, we provide an analysis of selforganized network management, with an end-to-end perspective of the network. Self-organization as applied to cellular networks is usually referred to Self-organizing Networks (SONs), and it is a key driver for improving Operations, Administration, and Maintenance (OAM) activities. SON aims at reducing the cost of installation and management of 4G and future 5G networks, by simplifying operational tasks through the capability to configure, optimize and heal itself. To satisfy 5G network management requirements, this autonomous management vision has to be extended to the end to end network. In literature and also in some instances of products available in the market, Machine Learning (ML) has been identified as the key tool to implement autonomous adaptability and take advantage of experience when making decisions. In this paper, we survey how network management can significantly benefit from ML solutions. We review and provide the basic concepts and taxonomy for SON, network management and ML. We analyse the available state of the art in the literature, standardization, and in the market. We pay special attention to 3rd Generation Partnership Project (3GPP) evolution in the area of network management and to the data that can be extracted from 3GPP networks, in order to gain knowledge and experience in how the network is working, and improve network performance in a proactive way. Finally, we go through the main challenges associated with this line of research, in both 4G and in what 5G is getting designed, while identifying new directions for research.

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“…We consider the analysis of our previous works [14]- [16], where the exploitation of the huge amount of data is analyzed with regression models to make better decisions for management purposes. In particular, our work in [15] focuses on two families of regression models, linear and nonlinear.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Conflict Resolution in Mobile Networks: A Self-Coordination Framework Based on Non-Dominated Solutions and Machine Learning for Data Analytics [Application Notes]

Moysen

García-Lozano

Giupponi

et al. 2018

IEEE Comput. Intell. Mag.

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Self-organizing network (SON) is a well-known term used to describe an autonomous cellular network. SON functionalities aim at improving network operational tasks through the capability to configure, optimize and heal itself. However, as the deployment of independent SON functions increases, the number of dependencies between them also grows. This work proposes a tool for efficient conflict resolution based on network performance predictions. Unlike other state-of-the-art solutions, the proposed self-coordination framework guarantees the right selection of network operation even if conflicting SON functions are running in parallel. This self-coordination is based on the history of network measurements, Corresponding Author: Jessica Moysen (Email: jessica.moysen@tsc.upc.edu) which helps to optimize conflicting objectives with low computational complexity. To do this, machine learning (ML) is used to build a predictive model, and then we solve the SON conflict by optimizing more than one objective function simultaneously. Without loss of generality, we present an analysis of how the proposed scheme provides a solution to deal with the potential conflicts between two of the most important SON functions in the context of mobility, namely mobility load balancing (MLB) and mobility robustness optimization (MRO), which require the updating of the same set of handover parameters. The proposed scheme allows fast performance evaluations when the optimization is running. This is done by shifting the complexity to the creation of a prediction model that uses historical data and that allows to anticipate the network performance. The simulation results demonstrate the ability of the proposed scheme to find a compromise among conflicting actions, and show it is possible to improve the overall system throughput.

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A Mobile Network Planning Tool Based on Data Analytics

Cited by 20 publications

References 22 publications

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

From 4G to 5G: Self-organized network management meets machine learning

Conflict Resolution in Mobile Networks: A Self-Coordination Framework Based on Non-Dominated Solutions and Machine Learning for Data Analytics [Application Notes]

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