Artificial neural networks for location estimation and co-cannel interference suppression in cellular networks

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.This project was funded by Solectron (Scotland) Ltd

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

PlaNet - Photo Geolocation with Convolutional Neural Networks

Is it possible to build a system to determine the location where a photo was taken using just its pixels? In general, the problem seems exceptionally difficult: it is trivial to construct situations where no location can be inferred. Yet images often contain informative cues such as landmarks, weather patterns, vegetation, road markings, and architectural details, which in combination may allow one to determine an approximate location and occasionally an exact location. Websites such as GeoGuessr and View from your Window suggest that humans are relatively good at integrating these cues to geolocate images, especially en-masse. In computer vision, the photo geolocation problem is usually approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. While previous approaches only recognize landmarks or perform approximate matching using global image descriptors, our model is able to use and integrate multiple visible cues. We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman levels of accuracy in some cases. Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, we demonstrate that this model achieves a 50% performance improvement over the single-image model

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Emergency response, the built environment and GPS signal quality: simulation and analysis of urban canyons in Quebec City

L’objectif général de cette recherche est de synthétiser les informations disponibles sur le développement d’un système d’urgence 9-1-1 pour les téléphones cellulaires dans le contexte nord américain. L’objectif spécifique du projet est de proposer une méthodologie qui détermine les conditions moyennes d’obstruction causée par les bâtiments qui nuisent à la qualité de la réception de signal GPS. Un modèle statistique de la qualité de signal GPS basé sur une campagne de mesures de réception des signaux GPS dans les arrondissements à caractère urbain dans la Ville de Québec (Canada) est employé pour simuler l’effet d’obstruction. Ces mesures ont montré une variabilité spatiale de la qualité de signal selon les conditions locales d’obstruction des édifices sur la voûte céleste. Une augmentation du pourcentage de ciel obstrué (effet de masque) a entraîné une augmentation de la probabilité de perte de signal GPS. Des cartes continues de la probabilité de perte de signal GPS ont été créés pour des feuillets de la Base de données topographiques de Québec au 1 : 20 000 en employant la technique d’interpolation spatiale par la méthode de la distance inverse pondérée (DIP).The general objective of this investigation is to extract the most pertinent information currently available on developing an emergency 9-1-1 system for cellular phones in the North American context. The specific objective of this project is to propose a methodology for determining the average obstruction by buildings which affect GPS satellite signal quality. A statistical model of GPS signal quality based on a field measurement campaign in the urban districts of Quebec City (Canada) was used to simulate this phenomenon. The measurements demonstrated a spatial variation in signal quality according to the building obstruction over the local sky. An increase in the percent of obstructed sky led to an increase in the probability of losing GPS signal lock. Continuous maps of GPS signal loss probability were created for sheets of the Quebec topographic database at the 1:20,000 scale using the Inverse Distance Weighting technique of spatial interpolation (IDW)

Proceedings of the Fifth International Mobile Satellite Conference 1997

Satellite-based mobile communications systems provide voice and data communications to users over a vast geographic area. The users may communicate via mobile or hand-held terminals, which may also provide access to terrestrial communications services. While previous International Mobile Satellite Conferences have concentrated on technical advances and the increasing worldwide commercial activities, this conference focuses on the next generation of mobile satellite services. The approximately 80 papers included here cover sessions in the following areas: networking and protocols; code division multiple access technologies; demand, economics and technology issues; current and planned systems; propagation; terminal technology; modulation and coding advances; spacecraft technology; advanced systems; and applications and experiments

Mobile and Wireless Communications

Mobile and Wireless Communications have been one of the major revolutions of the late twentieth century. We are witnessing a very fast growth in these technologies where mobile and wireless communications have become so ubiquitous in our society and indispensable for our daily lives. The relentless demand for higher data rates with better quality of services to comply with state-of-the art applications has revolutionized the wireless communication field and led to the emergence of new technologies such as Bluetooth, WiFi, Wimax, Ultra wideband, OFDMA. Moreover, the market tendency confirms that this revolution is not ready to stop in the foreseen future. Mobile and wireless communications applications cover diverse areas including entertainment, industrialist, biomedical, medicine, safety and security, and others, which definitely are improving our daily life. Wireless communication network is a multidisciplinary field addressing different aspects raging from theoretical analysis, system architecture design, and hardware and software implementations. While different new applications are requiring higher data rates and better quality of service and prolonging the mobile battery life, new development and advanced research studies and systems and circuits designs are necessary to keep pace with the market requirements. This book covers the most advanced research and development topics in mobile and wireless communication networks. It is divided into two parts with a total of thirty-four stand-alone chapters covering various areas of wireless communications of special topics including: physical layer and network layer, access methods and scheduling, techniques and technologies, antenna and amplifier design, integrated circuit design, applications and systems. These chapters present advanced novel and cutting-edge results and development related to wireless communication offering the readers the opportunity to enrich their knowledge in specific topics as well as to explore the whole field of rapidly emerging mobile and wireless networks. We hope that this book will be useful for students, researchers and practitioners in their research studies

Investigation of Shadow Matching for GNSS Positioning in Urban Canyons

All travel behavior of people in urban areas relies on knowing their position. Obtaining position has become increasingly easier thanks to the vast popularity of ‘smart’ mobile devices. The main and most accurate positioning technique used in these devices is global navigation satellite systems (GNSS). However, the poor performance of GNSS user equipment in urban canyons is a well-known problem and it is particularly inaccurate in the cross-street direction. The accuracy in this direction greatly affects many applications, including vehicle lane identification and high-accuracy pedestrian navigation. Shadow matching is a new technique that helps solve this problem by integrating GNSS constellation geometries and information derived from 3D models of buildings. This study brings the shadow matching principle from a simple mathematical model, through experimental proof of concept, system design and demonstration, algorithm redesign, comprehensive experimental tests, real-time demonstration and feasibility assessment, to a workable positioning solution. In this thesis, GNSS performance in urban canyons is numerically evaluated using 3D models. Then, a generic two-phase 6-step shadow matching system is proposed, implemented and tested against both geodetic and smartphone-grade GNSS receivers. A Bayesian technique-based shadow matching is proposed to account for NLOS and diffracted signal reception. A particle filter is designed to enable multi-epoch kinematic positioning. Finally, shadow matching is adapted and implemented as a mobile application (app), with feasibility assessment conducted. Results from the investigation confirm that conventional ranging-based GNSS is not adequate for reliable urban positioning. The designed shadow matching positioning system is demonstrated complementary to conventional GNSS in improving urban positioning accuracy. Each of the three generations of shadow matching algorithm is demonstrated to provide better positioning performance, supported by comprehensive experiments. In summary, shadow matching has been demonstrated to significantly improve urban positioning accuracy; it shows great potential to revolutionize urban positioning from street level to lane level, and possibly meter level