906 research outputs found
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
An Efficient Approach Formulation of Social Groups of User Calls of GSM
We are living in a world of wireless technology. The most widely used wireless i.e. mobile computing device today is the Mobile phone, can be used not only for voice and data communications but also as a computing device running context aware applications. In this paper we present a model that based on GSM data base. The objective of this paper identifies social and suspicious groups based on Cell Id, IMSI, IMEI, date and time, Location Area, MCC and MNC. This information can be used by applications for the detection of users, user context, discovering of groups and relation between them using clustering technique of data mining. One of the vital means in dealing with these data is to classify or group them into a set of categories or clusters. We demonstrate that even without knowledge of observed cell tower locations, we can recognize mobility modes that are useful for several application domains. Our mobility detection system was evaluated with GSM traces from the everyday lives of three data collector
Space-partitioning with cascade-connected ANN structures for positioning in mobile communication systems
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. Locationaware
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 crossenvironment
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
Analyses of location-based services in Africa and investigating methods of improving its accuracy
The subject area of this thesis analyses the provision of location-based services (LBS) in Africa
and seeks methods of improving their positional accuracy. The motivation behind this work is
based on the fact that mobile technology is the only modern form of information and
communication technology available to most people in Africa. Therefore all services that can be
offered on the mobile network should be harnessed and LBS are one of these services. This
research work is novel and is the first critical analysis carried out on LBS in Africa; therefore it
had to be carried out in phases.
A study was first carried out to analyse the provision of LBS in Africa. It was discovered that
Africa definitely lags much of the World in the provision of LBS to its mobile subscribers; only
a few LBS are available and these are not adapted to the needs of the African people. A field data
empirical investigation was carried out in South Africa to evaluate the performance of LBS
provided. Data collected indicated that the LBS provided is not dependable due to the inaccuracy
introduced by two major factors - the positioning method and the data content provided.
Analyzing methods to improve the positional accuracy proved quite challenging because Africa
being one of the poorest continents has most mobile subscribers using basic mobile phones.
Consequently, LBS often cannot be provided in Africa based on the capability of the mobile
phones but rather on the capability of the mobile operator’s infrastructure. However, provision of
LBS using the network-based positioning technologies poses the challenge of dynamically
varying error sources which affects its accuracy.
The effect of some error sources on network-based positioning technologies were analysed and a
model developed to investigate the feasibility of making the RSS-based geometric positioning
technologies error aware. Major consideration is given to the geometry of the BSs whose
measurements are used for position estimation.
Results indicated that it is feasible to improve location information in Africa not just by
improving the positioning algorithms but also by using improved prediction algorithms,
incorporating up-to-date geographical information and hybrid technologies. It was also
confirmed that although errors are introduced due to location estimation methods, it is impossible
to model the error and make it applicable for all algorithms and all location estimations. This is
because the errors are dynamically varying and unpredictable for every measurement
Non-Intrusive Subscriber Authentication for Next Generation Mobile Communication Systems
Merged with duplicate record 10026.1/753 on 14.03.2017 by CS (TIS)The last decade has witnessed massive growth in both the technological development, and
the consumer adoption of mobile devices such as mobile handsets and PDAs. The recent
introduction of wideband mobile networks has enabled the deployment of new services
with access to traditionally well protected personal data, such as banking details or
medical records. Secure user access to this data has however remained a function of the
mobile device's authentication system, which is only protected from masquerade abuse by
the traditional PIN, originally designed to protect against telephony abuse.
This thesis presents novel research in relation to advanced subscriber authentication for
mobile devices. The research began by assessing the threat of masquerade attacks on
such devices by way of a survey of end users. This revealed that the current methods of
mobile authentication remain extensively unused, leaving terminals highly vulnerable to
masquerade attack. Further investigation revealed that, in the context of the more
advanced wideband enabled services, users are receptive to many advanced
authentication techniques and principles, including the discipline of biometrics which
naturally lends itself to the area of advanced subscriber based authentication.
To address the requirement for a more personal authentication capable of being applied
in a continuous context, a novel non-intrusive biometric authentication technique was
conceived, drawn from the discrete disciplines of biometrics and Auditory Evoked
Responses. The technique forms a hybrid multi-modal biometric where variations in the
behavioural stimulus of the human voice (due to the propagation effects of acoustic
waves within the human head), are used to verify the identity o f a user. The resulting
approach is known as the Head Authentication Technique (HAT).
Evaluation of the HAT authentication process is realised in two stages. Firstly, the
generic authentication procedures of registration and verification are automated within a
prototype implementation. Secondly, a HAT demonstrator is used to evaluate the
authentication process through a series of experimental trials involving a representative
user community. The results from the trials confirm that multiple HAT samples from
the same user exhibit a high degree of correlation, yet samples between users exhibit a
high degree of discrepancy. Statistical analysis of the prototypes performance realised
early system error rates of; FNMR = 6% and FMR = 0.025%. The results clearly
demonstrate the authentication capabilities of this novel biometric approach and the
contribution this new work can make to the protection of subscriber data in next
generation mobile networks.Orange Personal Communication Services Lt
Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
none5noNatural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures.openEsposito M.; Palma L.; Belli A.; Sabbatini L.; Pierleoni P.Esposito, M.; Palma, L.; Belli, A.; Sabbatini, L.; Pierleoni, P
Probabilistic modelling and inference of human behaviour from mobile phone time series
With an estimated 4.1 billion subscribers around the world, the mobile phone offers a unique
opportunity to sense and understand human behaviour from location, co-presence and communication
data. While the benefit of modelling this unprecedented amount of data is widely
recognised, a number of challenges impede the development of accurate behaviour models. In
this thesis, we identify and address two modelling problems and show that their consideration
improves the accuracy of behaviour inference.
We first examine the modelling of long-range dependencies in human behaviour. Human behaviour
models only take into account short-range dependencies in mobile phone time series.
Using information theory, we quantify long-range dependencies in mobile phone time series for
the first time, demonstrate that they exhibit periodic oscillations and introduce novel tools to
analyse them. We further show that considering what the user did 24 hours earlier improves
accuracy when predicting user behaviour five hours or longer in advance.
The second problem that we address is the modelling of temporal variations in human behaviour.
The time spent by a user on an activity varies from one day to the next. In order to
recognise behaviour patterns despite temporal variations, we establish a methodological connection
between human behaviour modelling and biological sequence alignment. This connection
allows us to compare, cluster and model behaviour sequences and introduce novel features for
behaviour recognition which improve its accuracy.
The experiments presented in this thesis have been conducted on the largest publicly available
mobile phone dataset labelled in an unsupervised fashion and are entirely repeatable. Furthermore,
our techniques only require cellular data which can easily be recorded by today's mobile
phones and could benefit a wide range of applications including life logging, health monitoring,
customer profiling and large-scale surveillance
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