Space-partitioning with cascade-connected ANN structures for positioning in mobile communication systems

Borenović, M. 2010. Space-partitioning with cascade-connected ANN structures for positioning in mobile communication systems. PhD thesis University of Westminster School of Electronics and Computer Science https://doi.org/10.34737/904w7

TitleSpace-partitioning with cascade-connected ANN structures for positioning in mobile communication systems
TypePhD thesis
AuthorsBorenović, M.
Abstract

The world around us is getting more connected with each day passing by – new portable devices employing wireless connections to various networks wherever one might be. Location aware computing has become an important bit of telecommunication services and industry. For this reason, the research efforts on new and improved localisation algorithms are constantly being performed. Thus far, the satellite positioning systems have achieved highest popularity and penetration regarding the global position estimation. In spite the numerous investigations aimed at enabling these systems to equally procure the position in both indoor and outdoor environments, this is still a task to be completed.
This research work presented herein aimed at improving the state-of-the-art positioning techniques through the use of two highly popular mobile communication systems: WLAN and public land mobile networks. These systems already have widely deployed network structures (coverage) and a vast number of (inexpensive) mobile clients, so using them for additional, positioning purposes is rational and logical.
First, the positioning in WLAN systems was analysed and elaborated. The indoor test-bed, used for verifying the models’ performances, covered almost 10,000m2 area. It has been chosen carefully so that the positioning could be thoroughly explored. The measurement campaigns performed therein covered the whole of test-bed environment and gave insight into location dependent parameters available in WLAN networks. Further analysis of the data lead to developing of positioning models based on ANNs.
The best single ANN model obtained 9.26m average distance error and 7.75m median distance error. The novel positioning model structure, consisting of cascade-connected ANNs, improved those results to 8.14m and 4.57m, respectively. To adequately compare the proposed techniques with other, well-known research techniques, the environment positioning error
parameter was introduced. This parameter enables to take the size of the test environment into account when comparing the accuracy of the indoor positioning techniques.
Concerning the PLMN positioning, in-depth analysis of available system parameters and signalling protocols produced a positioning algorithm, capable of fusing the system received signal strength parameters received from multiple systems and multiple operators. Knowing that most of the areas are covered by signals from more than one network operator and even more than one system from one operator, it becomes easy to note the great practical value of this novel algorithm. On the other hand, an extensive drive-test measurement campaign, covering more than 600km in the central areas of Belgrade, was performed. Using this algorithm and applying the single ANN models to the recorded measurements, a 59m average distance error and 50m median distance error were obtained. Moreover, the positioning in indoor environment was verified and the degradation of performances, due to the cross environment model use, was reported: 105m average distance error and 101m median distance error.
When applying the new, cascade-connected ANN structure model, distance errors were reduced to 26m and 2m, for the average and median distance errors, respectively. The obtained positioning accuracy was shown to be good enough for the implementation of a broad scope of location based services by using the existing and deployed, commonly available, infrastructure.

Year2010
File
PublisherUniversity of Westminster
Publication dates
Published2010
Digital Object Identifier (DOI)https://doi.org/10.34737/904w7

Related outputs

Multi-system-multi-operator localization in PLMN using neural networks
Borenović, M., Neskovic, A. and Budimir, D. 2012. Multi-system-multi-operator localization in PLMN using neural networks. International Journal of Communication Systems. 25 (2), pp. 67-83. https://doi.org/10.1002/dac.1252

Space partitioning strategies for indoor WLAN positioning with cascade-connected ANN structures
Borenović, M., Neskovic, A. and Budimir, D. 2011. Space partitioning strategies for indoor WLAN positioning with cascade-connected ANN structures. International Journal of Neural Systems. 21 (1), pp. 1-15. https://doi.org/10.1142/S0129065711002614

Cross-system localization in PLMN using neural networks
Borenović, M., Neskovic, A. and Budimir, D. 2010. Cross-system localization in PLMN using neural networks. in: IEEE Radio and Wireless Symposium (RWS 2010), 10-14, January 2010, Sheraton hotel, New Orleans, LA IEEE . pp. 168-171

Cascade-connected ANN structures for indoor WLAN positioning
Borenović, M., Neskovic, A. and Budimir, D. 2009. Cascade-connected ANN structures for indoor WLAN positioning. in: Corchado, E. and Yin, H. (ed.) Intelligent Data Engineering and Automated Learning - IDEAL 2009: Proceedings of the 10th International Conference, Burgos, Spain, September 23-26, 2009 Springer.

Utilizing artificial neural networks for WLAN positioning
Borenović, M., Neskovic, A., Budimir, D. and Zezelj, L. 2008. Utilizing artificial neural networks for WLAN positioning. IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC'08). Cannes, France 15 - 18 Sep 2008 IEEE . https://doi.org/10.1109/PIMRC.2008.4699620

Permalink - https://westminsterresearch.westminster.ac.uk/item/904w7/space-partitioning-with-cascade-connected-ann-structures-for-positioning-in-mobile-communication-systems


Share this

Usage statistics

65 total views
77 total downloads
These values cover views and downloads from WestminsterResearch and are for the period from September 2nd 2018, when this repository was created.