Application of Classification and Regression Trees for Paging Traffic Prediction in LAC Planning

Hecker, Andreas; Kürner, Thomas

doi:10.1109/vetecs.2007.189

Cited by 10 publications

(2 citation statements)

References 11 publications

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“…Hecker and Kürner [4], an automatic method is introduced for the location area code planning prediction values of the probable signaling traffic which are required for mobile networks. Traffic and mobility needs to provide data.…”

Section: Literature Surveymentioning

confidence: 99%

Analysis of Prediction Techniques for Temporal Data Based on Nonlinear Regression Model

Sagar¹,

Gupta²,

Kashyap

2020

Advances in Intelligent Systems and Computing

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This paper depicts algorithms for the prediction of frequent data item that is: frequent item prediction method (FIPM) and frequent temporal pattern data stream (FTP-DS) based on linear and nonlinear regression algorithms, which are used to predict the trends in sequence with stream data. Sliding window is used for gathering and preprocessing real-time stream data. FIPM and FTPDS algorithms compute Support(y) for appointed sequence and describe linear and nonlinear equations to forecast sequence trends in the future. In this paper, the system used linear and nonlinear regression-based algorithms to evaluate the mean square error (MSE) during prediction.

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Section: Literature Surveymentioning

confidence: 99%

Analysis of Prediction Techniques for Temporal Data Based on Nonlinear Regression Model

Sagar¹,

Gupta²,

Kashyap

2020

Advances in Intelligent Systems and Computing

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“…Statistical performance counters have already been used for analysis and estimation of mobile terminated calls, paging, HO and location update (LU) traffic figures [3], [8], [9], [10]. Application examples are planning of connections of cells to MSCs for reasons of minimising inter-MSC HO or planning of registration areas (RA) aiming at a minimisation of paging and registration update (RU) traffic.…”

Section: Statistical Performance Countersmentioning

confidence: 99%

Introduction of Measurement-Based Estimation of Handover Attempts for Automatic Planning of Mobile Radio Networks

Hecker

Kürner

2009

2009 IEEE International Conference on Communications

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The complexity of current mobile radio networks (GERAN, UTRAN) has raised a demand for automatic planning procedures and a call for self-planning in future systems (LTE second phase). However, for any kind of automatism, reliable input data is necessary and, among other things, derivable from measurements of statistical counters collected by the operation and maintenance centre (OMC). Mobility information is required by planning procedures concerning registration areas, handover (HO) parameters, etc., in particular, and can mainly be derived from HO statistics. For a new network plan, this information has to be estimated. In this paper, an estimation method for HO attempts based on corresponding HO measurements from the network in operation is proposed. The associated model is based on graphs representing the interdependency of cells. The order of neighbourhood is introduced as a weight function for the edges. For appropriate weighting during estimation a general Pareto probability density function is determined from HO measurements. The analysis of the estimation performance is carried out by comparing the estimation values with corresponding OMC measurement data. Both the required network data and the OMC measurement data are provided by a real network.

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Deployment, Optimisation and Operation of Next Generation Networks

Kürner

Grazioso

Eisenblätter

et al. 2012

Signals and Communication Technology

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Application of Classification and Regression Trees for Paging Traffic Prediction in LAC Planning

Cited by 10 publications

References 11 publications

Analysis of Prediction Techniques for Temporal Data Based on Nonlinear Regression Model

Analysis of Prediction Techniques for Temporal Data Based on Nonlinear Regression Model

Introduction of Measurement-Based Estimation of Handover Attempts for Automatic Planning of Mobile Radio Networks

Deployment, Optimisation and Operation of Next Generation Networks

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