Approaches for the classification of traffic and radio resource management in mobile cellular networks : an application to South Africa
type: Dataset
description: The growth in the number of cellular mobile subscribers worldwide has far outpaced expected rates of growth with worldwide mobile subscriptions reaching 6 Billion subscribers in 2011 according to the International Telecommunication Union (ITU). More than 75% of this figure is in developing countries. With this rate of growth, greater pressure is placed on radio resources in mobile networks which impacts on the quality and grade of service (GOS) in the network. With varying demands that are generated from different subscriber classes in a network, the ability to distinguish between subscriber types in a network is vital to optimise infrastructure and resources in a mobile network. In this study, a new approach for subscriber classification in mobile cellular networks is proposed. In the proposed approach, traffic data extracted from two network providers in South Africa is considered. The traffic data is first decomposed using traditional feature extraction approaches such as the Empirical Mode Decomposition (EMD) and the Discrete Wavelet Packet Transform (DWPT) approach. The results are then compared with the Difference Histogram approach which considers the number of segments of increase in the time series. Based on the features extracted, classification is then achieved by making use of a Fuzzy C-means algorithm. It is shown from the results obtained that a clear separation between subscriber classes based on inputted traffic signals is possible through the proposed approach. Further, based on the subscriber classes extracted, a novel two-level hybrid channel allocation approach is proposed that makes use of a Mixed Integer Linear Programming (MILP) model to consider the optimisation of radio resources in a mobile network. In the proposed model, two levels of channel allocation are considered: the first considers defining a fixed threshold of channels allocated to each cell in the network. The second level considers a dynamic channel allocation model to account fo…
identifier: NNT: 2012PEST1090
issued: 2026-05-14T03:54:26.004025
language: en
modified: 2026-05-14T03:54:26.004030
contact point: Organization
  fn: CCSD
keyword: classification-des-abonnes
keyword: extraction-des-attributs
keyword: feature-extraction
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: mobile-networks
keyword: network-optimization
keyword: optimisation-des-resources-reseau
keyword: reseaux-mobiles
keyword: subscriber-classification
keyword: theses
landing page: https://theses.hal.science/tel-00794515

Approaches for the classification of traffic and radio resource management in mobile cellular networks : an application to South Africa
type: Distribution
format: HTML
issued: 2026-05-14T03:54:26.046677
modified: 2026-05-14T03:54:25.973274
access URL: https://theses.hal.science/tel-00794515

test_moissonnage_selune
type: Agent