Telecommunication has experienced significant changes over the past few years and its paradigm has moved from wired to wireless communications. The wireless channel constitutes the basic physical link between the transmitter and the receiver antennas. Therefore, complete knowledge of the wireless channel and radio propagation environment is necessary in order to design efficient wireless communication systems. This PhD thesis is devoted to studying the spatial and temporal statistics of the wireless channel in cellular environments based on a geometry-based stochastic physical channel modeling approach. Contributions in this thesis report include the following: ??? A new physical channel model called the eccentro-scattering model is proposed to study the spatial and temporal statistics of the multipath signals in cellular environments. ??? Generic closed-form formulas for the probability density function (pdf) of angle of arrival (AoA) and time of arrival (ToA) of the multipath signals in each cellular environment are derived. These formulas can be helpful for the design and evaluation of modern communication systems. ??? A new Gaussian scattering model is proposed, which consists of two Gaussian functions for the distribution of scatterers around base station (BS) and mobile station (MS) and confines these scatterers within a scattering disc. ??? The effect of mobile motion on the spatial and temporal statistics of the multipath signals in cellular environments is discussed. Three motion scenarios are considered for the possible trajectory of the mobile unit. Furthermore, two different cases are identified when the terrain and clutter of mobile surrounding have additional effect on the temporal spread of the multipath signals during motion. ??? The physical channel model is employed to assess the performance of a RAKE receiver in cellular environments. ??? Comparisons between uniform scattering and Gaussian scattering, which are the two assumptions for the distribution of scatterers usually used in the derivation of the pdf of AoA, are also presented. ??? An overview of earlier physical channel models and comparisons between these models and with the proposed model are presented.

Thesis (M. Sc.)(Computer Science)--University of Pretoria, 2004. / Title from opening screen (viewed 10th March, 2005). Includes summary. Includes bibliographical references.

Huge amounts of data are being collected as a result of the increased use of mobile telecommunications. Insight into information and knowledge derived from these databases can give operators a competitive edge in terms of customer care and retention,<br /> marketing and fraud detection. One of the strategies for fraud detection checks for signs of questionable changes in user behavior. Although the intentions of the mobile phone users cannot be observed, their intentions are reflected in the call data which define usage patterns. Over a period of time, an individual phone generates a large pattern of use. While call data are recorded for subscribers for billing purposes, we are making no prior assumptions about the data indicative of fraudulent call patterns, i.e. the calls made for billing purpose are unlabeled. Further analysis is thus, required to be able to isolate fraudulent usage. An unsupervised learning algorithm can analyse and cluster call patterns for each subscriber in order to facilitate the fraud detection process.<br /> <br /> This research investigates the unsupervised learning potentials of two neural networks for the profiling of calls made by users over a period of time in a mobile telecommunication network. Our study provides a comparative analysis and application of Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) recurrent neural networks algorithms to user call data records in order to conduct a descriptive data mining on users call patterns.<br /> <br /> Our investigation shows the learning ability of both techniques to discriminate user call patterns / the LSTM recurrent neural network algorithm providing a better discrimination than the SOM algorithm in terms of long time series modelling. LSTM discriminates different types of temporal sequences and groups them according to a variety of features. The ordered features can later be interpreted and labeled according to specific requirements of the mobile service provider. Thus, suspicious call behaviours are isolated within the mobile telecommunication network and can be used to to identify fraudulent call patterns. We give results using masked call data<br /> from a real mobile telecommunication network.

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 122-126). Available also in a digital version from Dissertation Abstracts.

Huge amounts of data are being collected as a result of the increased use of mobile telecommunications. Insight into information and knowledge derived from these databases can give operators a competitive edge in terms of customer care and retention,<br /> marketing and fraud detection. One of the strategies for fraud detection checks for signs of questionable changes in user behavior. Although the intentions of the mobile phone users cannot be observed, their intentions are reflected in the call data which define usage patterns. Over a period of time, an individual phone generates a large pattern of use. While call data are recorded for subscribers for billing purposes, we are making no prior assumptions about the data indicative of fraudulent call patterns, i.e. the calls made for billing purpose are unlabeled. Further analysis is thus, required to be able to isolate fraudulent usage. An unsupervised learning algorithm can analyse and cluster call patterns for each subscriber in order to facilitate the fraud detection process.<br /> <br /> This research investigates the unsupervised learning potentials of two neural networks for the profiling of calls made by users over a period of time in a mobile telecommunication network. Our study provides a comparative analysis and application of Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) recurrent neural networks algorithms to user call data records in order to conduct a descriptive data mining on users call patterns.<br /> <br /> Our investigation shows the learning ability of both techniques to discriminate user call patterns / the LSTM recurrent neural network algorithm providing a better discrimination than the SOM algorithm in terms of long time series modelling. LSTM discriminates different types of temporal sequences and groups them according to a variety of features. The ordered features can later be interpreted and labeled according to specific requirements of the mobile service provider. Thus, suspicious call behaviours are isolated within the mobile telecommunication network and can be used to to identify fraudulent call patterns. We give results using masked call data<br /> from a real mobile telecommunication network.

Thesis (PhD)--University of South Australia, 2008.

This thesis presents the findings from a study on the adoption of mobile phones across ASEAN countries.

Thesis (M.App.Sc.) - Carleton University, 2002. / Includes bibliographical references (p. 85-87). Also available in electronic format on the Internet.

Thesis (M. App. Sc.)--Carleton University, 2005. / Includes bibliographical references (p. 63-64). Also available in electronic format on the Internet.

Thesis (Ph. D.)--State University of New York at Buffalo, 2002. / "May 10, 2002." Includes bibliographical references (p. 140-150). Also available in print.