|Appears in Collections:
|Computing Science and Mathematics eTheses
|Artificial neural networks for location estimation and co-cannel interference suppression in cellular networks
mobile location determination
co-channel interference suppression
|University of Stirling
|3. J. Muhammad, A. Hussain, Alexander Neskovic & Evan Magill, "New Neural Network Based Mobile Location Estimation in a Metropolitan Area", Book Chapter, in Lecture Notes in Computer Science (LNCS), Springer Berlin / Heidelberg, ISSN: 0302-9743, Volume 3697/2005, pages 935-941, Artificial Neural Networks: Formal Models and Their Applications - ICANN 2005 , ISBN: 978-3-540-28755-1
2. J. Muhammad, A. Hussain & W.Ahmed, "New Neural Network based Mobile Location Estimation in Urban Propagation Models", Proceedings 7th IEEE International Multi-Topic Conference (INMIC'2003), Islamabad, 8-9 Dec, 2003
1. J. Muhammad, A. Hussain & W. Ahmad, "Location Estimation in Cellular Networks using Neural Networks", Proceedings 1st IEEE-IEE-ESF International Workshop on Signal Processing for Wireless Communication (SPWC'2003), pages 243-247, King's College, London, 19-20 May, 2003
|This thesis reports on the application of artificial neural networks to two important problems encountered in cellular communications, namely, location estimation and co-channel interference suppression. The prediction of a mobile location using propagation path loss (signal strength) is a very difficult and complex task. Several techniques have been proposed recently mostly based on linearized, geometrical and maximum likelihood methods. An alternative approach based on artificial neural networks is proposed in this thesis which offers the advantages of increased flexibility to adapt to different environments and high speed parallel processing. Location estimation provides users of cellular telephones with information about their location. Some of the existing location estimation techniques such as those used in GPS satellite navigation systems require non-standard features, either from the cellular phone or the cellular network. However, it is possible to use the existing GSM technology for location estimation by taking advantage of the signals transmitted between the phone and the network. This thesis proposes the application of neural networks to predict the location coordinates from signal strength data. New multi-layered perceptron and radial basis function based neural networks are employed for the prediction of mobile locations using signal strength measurements in a simulated COST-231 metropolitan environment. In addition, initial preliminary results using limited available real signal-strength measurements in a metropolitan environment are also reported comparing the performance of the neural predictors with a conventional linear technique. The results indicate that the neural predictors can be trained to provide a near perfect mapping using signal strength measurements from two or more base stations. The second application of neural networks addressed in this thesis, is concerned with adaptive equalization, which is known to be an important technique for combating distortion and Inter-Symbol Interference (ISI) in digital communication channels. However, many communication systems are also impaired by what is known as co-channel interference (CCI). Many digital communications systems such as digital cellular radio (DCR) and dual polarized micro-wave radio, for example, employ frequency re-usage and often exhibit performance limitation due to co-channel interference. The degradation in performance due to CCI is more severe than due to ISI. Therefore, simple and effective interference suppression techniques are required to mitigate the interference for a high-quality signal reception. The current work briefly reviews the application of neural network based non-linear adaptive equalizers to the problem of combating co-channel interference, without a priori knowledge of the channel or co-channel orders. A realistic co-channel system is used as a case study to demonstrate the superior equalization capability of the functional-link neural network based Decision Feedback Equalizer (DFE) compared to other conventional linear and neural network based non-linear adaptive equalizers.
|Thesis or Dissertation
|School of Natural Sciences
Computing Science and Mathematics
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