000			06165cam a2200433M 4500
001			96567
003			ES-MaUEC
005			20230102112800.0
006			m o d
007			cr |n|||||||||
008			171004s2017 ii ob 000 0 eng d
020			_a8132237218 _q(electronic bk.)
020			_a9788132237211 _q(electronic bk.)
020			_z8132237196
020			_z9788132237198
040			_aYDX _beng _cYDX _dN$T _dEBLCP _dGW5XE _dN$T _dOTZ _dAZU _dUPM _dMERER _dOCLCF _dIOG _dCOO _dOCLCQ _dES-MaUEC _bspa
050		4	_aTK5103.48325 _b2017 EB
100	1		_aKakadia, Deepak.
245	1	0	_aNetwork performance and fault analytics for LTE wireless service providers _cDeepak Kakadia, Jin Yang, Alexander Gilgur.
264		1	_aNew Delhi _bSpringer International Publishing _c2017
300			_a1 recurso en línea
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF
500			_a _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas
505	0		_aPreface; About the Book; Contents; About the Authors; Abbreviations; 1 Network Performance and Fault Analytics for LTE Wireless Service Providers; 1.1 Introduction; 1.2 Motivation; 1.3 Current Performance and Fault Management Architectures; 1.4 Proposed Next-Generation Performance and Fault Management Architectures; 1.5 Summary of Gaps in Current Network Performance and Fault Tools and Analytics; 1.6 Book Outline; 2 Analytics Fundamentals; 2.1 Statistical Process Control; 2.1.1 Central Limit Theorem; 2.1.2 Applications of Central Limit Theorem: Bernoulli Trials
505	8		_a2.1.3 Examples of SPC for Bernoulli Trials2.2 Outliers; 2.2.1 QoS Outliers; 2.2.2 Outliers: What Are They?; 2.2.3 Outlier Detection: The Basic Approach; 2.2.4 Advanced Methods of Outlier Detection; 2.3 A Few Words About Queueing Systems; 2.3.1 "True" Process Distributions for LTE Network Components; 2.3.2 Little's Law: The "Big Three" of Queueing Dynamics; 2.3.3 System Performance Laws; 2.3.3.1 Amdahl's Law; 2.3.3.2 Universal Scalability Law; 2.3.4 Conclusion; 2.4 Forecasting; 2.4.1 Time Series: Definition and Assumptions; 2.4.2 Filling in the Gaps in Data; 2.4.2.1 Why?
505	8		_a2.4.2.2 Methods: Extension2.4.2.3 Methods: Interpolation; 2.4.3 Moving Averages; 2.4.4 EWMA; 2.4.5 EWMA Forecasting; 2.4.6 ARIMA Forecasting; 2.4.7 Selection of Forecasting Model; 2.5 Regression; 2.5.1 A Few Words on Terminology; 2.5.2 Linearizable Relationships; 2.5.3 The Main Idea Behind Regression; 2.5.4 Solving Eq. (2.5.2); 2.5.5 Goodness of Fit; 2.5.6 Model Competition; 2.5.7 Analysis of Residuals; 2.5.8 Advanced Regression Methods; 2.5.9 Do We Have to Compete?; 2.6 Clustering; 2.6.1 Bundling the Curves; 2.6.2 Geographic Clustering; 2.6.3 Geographic Clustering of Signal; 2.7 Conclusion
505	8		_a3.5.2 Retainability3.5.3 Integrity; 3.5.4 Availability; 3.5.5 Mobility; 3.6 Summary; 4 Enhanced Packet Core Network; 4.1 EPC Architecture; 4.1.1 SAE Gateways; 4.1.2 Mobility Management Entity; 4.1.3 Policy and Charging Resource Function (PCRF); 4.1.4 Home Subscription Server (HSS); 4.1.5 Application Services Domain (AS); 4.2 EPC Interfaces Specifications; 4.2.1 S1-U Interface; 4.2.2 S1-C Interface; 4.2.3 S5/S8 and SGi Interfaces; 4.2.4 Gx/Gxc and Rx Interfaces; 4.3 EPC Network Management Model; 4.3.1 MME Mobility Measurements; 4.3.2 MME Session and Subscriber Management
520	3		_aThis book is intended to describe how to leverage emerging technologies big data analytics and SDN, to address challenges specific to LTE and IP network performance and fault management data in order to more efficiently manage and operate an LTE wireless networks. The proposed integrated solutions permit the LTE network service provider to operate entire integrated network, from RAN to Core , from UE to application service, as one unified system and correspondingly collect and align disparate key metrics and data, using an integrated and holistic approach to network analysis. The LTE wireless network performance and fault involves the network performance and management of network elements in EUTRAN, EPC and IP transport components, not only as individual components, but also as nuances of inter-working of these components. The key metrics for EUTRAN include radio access network accessibility, retainability, integrity, availability and mobility. The key metrics for EPC include MME accessibility, mobility and capacity, SGW, PGW capacity and connectivity. In the first parts of the book, the authors describe fundamental analytics techniques, and various key network partitions - RAN, Backhaul, Metro and Core of a typical LTE Wireless Service Provider Network. The second part of the book develops more advanced analytic techniques that can be used to solve complex wireless network problems. The second part of this book also describes practical and novel solutions for LTE service network performance and fault management systems using big data engineering. Self-organizing network (SON) architecture is presented as a way to utilize network performance and fault analytics to enable network automation. SON can significantly improve operational efficiencies and speed up network deployment. This book provides various ways to leverage data science to more intelligently and reliably to automate and manage a wireless network. The contents of the book should be useful to professional engineers and networking experts involved in LTE network operations and management. The content will also be of interest to researchers, academic and corporate, interested in the developments in fault analytics in LTE networks.
650		7	_aLTE (Telecomunicaciones) _2embne _0(OCoLC)fst01762769 _0 _9471867
700	1		_aGilgur, Alexander.
700	1		_aYang, Jin.
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-81-322-3721-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017
998			_b02/2018 _dz _e- _zSI
999			_c96567 _d96567 _x1