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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 6, June 2011
Location Estimation and Mobility Prediction Using
Neuro-fuzzy Networks
In Cellular Networks
Maryam Borna Mohammad Soleimani
Department of Electrical Engineering Department of Electrical Engineering
Iran University of Science and Technology Iran University of Science and Technology
Tehran, Iran Tehran, Iran
maryam.borna@gmail.com soleimani@iust.ac.ir
Abstract- In this paper an approach is proposed for location However for managing networks resources consumption
estimation, tracking and mobility prediction in cellular and reducing the costs of location update and call delivery
networks in dense urban areas using neural and neuro-fuzzy procedures, prediction of user's next probable location can be
networks. In urban areas with high buildings, due to the effects helpful. This is done by analyzing some patterns of his
of multipath fading and Non-Line-of-Sight conditions, the mobility behavior. Therefore searching for users will be done
accuracy of positioning methods based on direction finding and in smaller groups of cells avoiding expensive queries to
ranging degrades significantly. Also in these areas, due to high Home Location Register (HLR). This is also useful in other
user traffic there's a need for network resources management. wireless networks such as Ad-Hoc networks for efficient
Knowing the next possible position of user would be helpful in
bandwidth allocation and uninterrupted hand over between
this case. Here using fingerprint positioning concept, after
choosing appropriate parameters for fingerprinting in GSM
access points.
cellular networks, MLP and RBF neural networks were used Next sections are as follows: section II describes the
for position estimation. Then by the use of neuro-fuzzy problem of positioning in dense urban areas and related
networks a tracking and post-processing method is applied to studies in the literature. In section III proposed approach of
estimated locations. For mobility prediction purpose the use of this paper for positioning and mobility prediction is
ANFIS neuro-fuzzy is implemented. explained and contains 3 subsections: fingerprint based
positioning in subsection A, post processing of estimated
Keywords-position estimation; neuro-fuzzy; prediction;
cellular networks.
path in subsection B and path prediction in subsection C are
discussed. Section IV includes the results of evaluating the
I. INTRODUCTION proposed approach on database collected from GSM mobile
phone network in city of Tehran. Results were discussed in
Positioning in wireless networks is estimating a node's section V and last section concludes the paper.
distance with reference to a fixed node or locating it by its
geographical coordinates. Positioning is based on parameters II. PROBLEM DEFINITION
used by mobile or fixed nodes for communication such as
Received Signal Strength (RSS), Time of Arrival (TOA) and With developments in cellular phone networks different
Angle of Arrival (AOA). According to the type of wireless methods were considered for facilitating user positioning,
network and transmission protocols, different parameters are such as Cell-ID, Cell-ID+TA, A-GPS, AOA, … the more
used for communication. accuracy increases the more expensive the deployment
would be and the need for hardware and software changes in
Among several types of wireless networks, cellular phone both cell phone device and network infrastructure rises.
networks due to increasing usage of cell phones for Moreover most of these methods are sensitive to Non Lin of
communications are more distributed with more subscribers Sight (NLOS) communication between transmitter and
so it can be said that one of the most probable items found in receiver and multipath fading, conditions that dense urban
everyone's pocket is his cell phone. Having location areas are involved with. Although everyday there are more
information in cellular phone networks, various services can and more mobile phone devices equipped with GPS receivers
be provided based on user's location ranging from with positioning accuracy up to few meters, but in urban
commercial and advertising services to routing, navigation areas with high buildings where it's less likely to have line of
and emergency calls. In cellular phone networks these sight communication with at least 3 GPS satellites, or inside
services are referred to as Location Based Services (LBS). buildings where the signals attenuate significantly passing
Also by locating a user's exact position network's resources through the walls, positioning accuracy degrades
can be efficiently managed and allocated leading to proper considerably. In such cases there's a need for an auxiliary
handover between cells and reduced co-channel interference. method to overcome these problems.
65 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 6, June 2011
The difficulty in these areas is the complexity of standards and data collection tool. Data collection and
propagation model of electromagnetic waves caused by pattern learning are done offline before online real-time
multipath fading, diffraction and scattering that makes it hard positioning.
for geometrical and statistical positioning methods relying on
relations between signal parameters and Tx-Rx separation. Input parameters of neural networks, fingerprint of a
point, must be measurable, collectable and different from
Fingerprint based positioning methods are better for place to place. Here we aim to attain intended fingerprints
mentioned cases [1] [2]. In these methods first a database of from information provided by mobile phone device without
signal parameters in certain places is collected with no software or hardware changes in the device and additional
knowledge of propagation model of the environment and signaling between MS and BTS. The data can be obtained
position estimation will be done upon these information and from mobile phone routing table. This table is used for
possible mapping among them. selecting the best cell to reside and is resorted every few
seconds. In GSM900 standard for mobile phone networks,
One way to find the mapping relations in the fingerprint this table contains a list of 30 radio channels (ARFCN)
database is to use Artificial Neural Networks. These sorted in descending order based on received power. In
networks are able to estimate the complex nonlinear addition to received power other parameters of currently
functions like mapping relations by parallel processing of selected and neighboring cells are available like cell name,
neurons. Position estimation can be considered as a function absolute radio frequency number for broadcasting cell's
approximation problem in neural networks that aims to find status, received power level, received signal quality and
the nonlinear mapping between inputs (fingerprints) and timing advance (TA). Also the attributes of BTS antennas of
outputs or targets (mobile phone's coordinates). In each cell like its height and installation coordinates are
comparison with other database lookup methods like K- accessible.
Nearest Neighbor (KNN) that uses fingerprint parameters to
find its nearest Euclidean neighbors, neural networks are From the mentioned parameters those were chosen for
better. On the other hand since neural networks approximate fingerprint that own following properties:
functions and fingerprint details are somewhat related to
delay and power loss of arrived signals and in turn these are • Being sensitive to spatial changes. Therefore
dependent on Tx-Rx separation, it seems that neural fixed parameters within a cell boundary like
networks combine both features of RSS and TOA-TDOA radio channel numbers, cell antenna height and
based systems [3] [4]. Two common models of neural similar parameters are not suitable for
networks in function approximation, multi layer perceptron fingerprinting.
(MLP) and Radial Basis Function (RBF) networks, are used • Parameters should be representative of
more [2] [3] [5] [6]. multipath fading effects in propagation
After user localization, history of his travelled places can environment. Received signal level, received
be considered as a time series that by recognizing his signal quality and Timing Advance (TA) are
mobility pattern, his next location can be predicted. In such parameters. However TA is a discrete
literature for user's path prediction in wireless networks with value of estimated BTS-MS separation with an
different standards, Recurrent Neural Networks (RNN), accuracy about 550 meters, say if TA=1, MS is
Bayesian Neural Networks (BNN) or neuro-fuzzy networks in a radius of 550 meters from BTS and TA=2
were employed that some used user's behavioral pattern in a means MS is in a radius of 550 to 1100 meters
long period of time and different situations to learn his from cell antenna. Hence in a cell with radius
mobility pattern and similar users then predict their next less than 550 meters-like most urban cells- TA's
location [8] [9] [10]. value isn't much helpful in positioning.
In this paper after gathering enough fingerprints with • For less signaling between mobile phone and
appropriate parameters in GSM cellular phone network, BTS it's better to acquire fingerprint in IDLE
positioning of mobile phone device is done by searching for mode rather that ACTIVE mode. TA and
the best architecture for MLP and RBF neural networks in a Received signal quality are determined in
dense urban area. Afterwards using tracking and prediction ACTIVE mode while RSS is monitored
feature of ANFIS neuro-fuzzy network, the estimated path is periodically even in IDLE mode.
post processed and user's upcoming path is predicted. We chose parameters that fulfill mentioned requirements
namely Received signal strength from cell antenna beside its
III. PROPOSED APPROACH
coordinates for serving cell and two of neighboring cells. So
A. Fingerprint based positioning there are 9 parameters to be recorded in a single fingerprint
In fingerprint based positioning methods first a database beside the coordinates of data gathering location. By
of fingerprints in a certain area is collected. A fingerprint of a collecting these fingerprints in sufficient data points of the
certain point includes particular information like designated area, we have a suitable database for further
geographical coordinates of the point which is a specification analysis by fingerprint based positioning methods. As
of that point. This information includes estimated signal discussed in previous section, for database processing, neural
parameters that are different depending on wireless network networks predominates other methods so is employed here.
Training set tuples are mentioned 9 parameters as input and
66 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 6, June 2011
latitude, longitude of respective data point as target or output
for neural networks.
One of the problems in neural networks design,
particularly MLP networks is the lack of certain equations
for determining the perfect architecture of the network and
number of neurons in hidden layers. In the training phase of
a NN while evaluating its ability to learn, its response to new
untrained data should also be considered for the network to
generalize well. In order that 84% of data set were used for Figure 2. Proposed architecture for RBF neural network
training and the remaining for testing the trained network.
For finding the best architecture for MLP NN first an
upper limit regarding members of training set is considered
for maximum number of network parameters i.e. total
number of weights and biases in neural network then by
modifying the number of hidden layers and neurons in each
layer in the defined range, the architecture yielding less
positioning error for training, testing and the whole data set
is chosen. For available database the best architecture for
MLP was one hidden layer with 23 neurons. Input layer
neurons were set to 9 and output layer neurons for estimated
latitude and longitude of mobile phone's location are 2. Fig. 1
In standard Radial Basis Function NN a ruling parameter Figure 3. Neuro-fuzzy network for Sugeno's fuzzy inference structure
in the design is the radial neuron's spread that determines its
sensitivity to the resemblance between network's inputs and Inputs of ANFIS network were estimated path with 5
weights. Searching for spread parameter resulted in value of delays and the same path with no delays as output. For initial
196 leading to less error for testing data set. The number of FIS generation fed to ANFIS, we used subtractive clustering
neurons has been set to its maximum i.e. the same as training that the influence radius of every cluster for all 11
set members. Fig.2 dimensions of data was set to 0.5.
Another type of RBF networks employed here is ANFIS is used here to estimate the user's movement
Generalized Regression Neural Network (GRNN) that has a function and smooth the NN estimated path so it can be
fixed structure with little difference to standard RBF specified which road the user is moving on, useful in map
networks. Spread parameter for radial neuron has been routing and navigation purpose.
obtained like former case and set to 2. C. Predicting user's next location
After position estimation with designed neural networks, Subsequent locations travelled by mobile phone user can
there were rather big errors in few points probably caused by be assumed as a time series. Here we used the prediction
inadequate members of training set and unavailability of RSS ability of ANFIS network. The structure is the same as
in some points. To lessen this error we applied a post before. For training the network, 20% of the beginning of the
procession on estimated path coming in next subsection. travelled path with 2 delays was selected as input and the
same path with one precession as output. The remaining 80%
B. Post-processing the estimated path of the path was used for testing. In this way trained network
In this section we use ANFIS (Adaptive Neuro-Fuzzy would be able to predict next location by knowing the
Inference Structure) for processing the previously NN present and one previous location of user. Here we've used
estimated path of user's travelled places. Employed neuro- the estimated path by neural network in section III and
fuzzy network is the neural network equivalent for Sugeno calculated the error with respect to real path.
FIS (Fuzzy Inference Structure). In comparison with MLP,
ANFIS has a fixed architecture and no searching for best IV. EVALUATION OF THE PROPOSED APPROACH
structure is needed. It responds faster with less computational For data collection we've used TEMS® drive tester tool
resource consumption. Fig. 3 that is used for optimization and troubleshooting of mobile
phone network by monitoring its status. It represents
network's data intercepted by mobile phone in a computer
interface for further processing and also able to record data
collection point coordinates via GPS receiver.
We've used this tool for fingerprint database collection in
GSM communication network in city of Tehran for about
250 data points. For network training and simulation we used
MATLAB® neural network and fuzzy logic toolboxes.
Figure 1. Proposed architecture for MLP neural network
67 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 6, June 2011
Simulation results of trained neural network showed that 51.48
Tajrish-Zarrabkhane
designed MLP network performs better than RBF networks, Real track
however RBF networks are faster and easier to design but are 51.475 Trained Track
Predicted Track
suitable when training set members are very high.
51.47
51.49 51.465
Real track
Longitude
Neural Network estimated 51.46
ANFIS post-processing
51.48
BTS position 51.455
51.45
51.47
longitude
51.445
51.46
51.44
35.74 35.75 35.76 35.77 35.78 35.79 35.8 35.81 35.82
Latitude
51.45
Figure 7. Proposed user's mobility prediction with ANFIS
51.44 Fig.4 displays estimated location after post processing
with ANFIS indicating mitigation of high errors. Fig.5 is
35.75 35.76 35.77 35.78 35.79 35.8 35.81 Cumulative Error Probability after and before post-
latitude
processing showing alleviation of high errors. In Fig.6 the
Figure 4. Position estimation and post-processing result in a road
same path is displayed on the map by Google Earth®. It can
be seen after ANFIS post-processing the road the user is
travelling can be defined more accurately.
1
0.9
Fig.7 displays real, trained and predicted path by ANFIS.
CEP-60% is less than 115 meters which makes this
Cumulative Distribution Function (CDF)
0.8
prediction useful in determination of user's next probable cell
0.7
to reside leading to better management of network resources
0.6
and successful cell reselection.
0.5
0.4 V. CONCLUSION
0.3 In proposed approach for location estimation in cellular
0.2 networks by neural networks in dense urban areas, mean
0.1
NN estimated positioning error less than 80 meters and CEP-60% of 65m
ANFIS post-processing
0
were obtained in a 3 by 4 km area that in comparison with
0 100 200 300 400 500
Positioning Error(m)
600 700 800 900 most commercial positioning methods implemented in
cellular networks like E-CGI, E-OTD and AOA with 200 m
Figure 5. Cumulative Error Probability before and after post-processing positioning error in such conditions, 50% improvement was
achieved. Meanwhile this method can be a complement to
GPS positioning in cases GPS signals are weak. In this
method there is no additional signaling or extra hardware-
software installation in both phone device and network.
We've used a fingerprint database of RSS parameters which
is available in most wireless networks. In comparison with
other positioning methods based on neural networks, we've
avoided a fixed structure for MLP NNs by searching for the
best one that suits certain database with a simple script.
Applying ANFIS post-processing by approximating user's
movement function, decreased high errors. The accuracy of
proposed mobility prediction by ANFIS with respect to
radius of cells in most cities that are about 100 to 150 m
makes it useful in anticipation of user's next cell to be
causing decrement in costs of location update and paging
procedure.
Figure 6. Part of the path of Fig. 4 on the map of Tehran
68 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 6, June 2011
REFERENCES AUTHORS PROFILE
[1] Ines Ahriz, Yacine Oussar, Bruce Denby, and Gerard Dreyfus, "Full- Maryam Borna has received her Bachelor of Science in Electrical
Band GSM Fingerprints for Indoor Localization Using a Machine Engineering with major of Telecommunications from Shahed University,
Learning Approach," International Journal of Navigation and Tehran, Iran and her Master of Science in IT Engineering with major of
Observation, 2010. Secure Communications from Dept. of Electrical Engineering of Iran
[2] Claude Takenga and Kyandoghere Kyamakya, "A Low-cost University of Science and Technology,Tehran,Iran. Her research interests
Fingerprint Positioning System in Cellular Networks," in Second include mobile phone networks, neural networks, microstrip antennas.
International Conference on Communications and Networking in Mohammad Soleimani received the B.S. degree in electrical
China,CHINACOM '07. , 2007. engineering from the University of Shiraz, Shiraz, Iran, in 1978 and the
[3] Anthony Taok, Nahi Kandil, and Sofiene Affes, "Neural Network for M.S. and Ph.D. degrees from Pierre and Marie Curio University, Paris,
Fingerprinting-Based Indoor Localization Using Ultra-Wideband," France, in 1981 and 1983, respectively. He is working as a Professor with
Journal of Communications, vol. 4, no. 4, 2009. the Iran University of Sciences and Technology, Tehran, Iran. His research
[4] M.H Hung, Shi-Shung Lin, Jui-Yu Cheng, and Wu-Lung Chien, "A interests are in antennas, small satellites, electromagnetic, and radars. He
ZigBee Indoor Positioning Scheme using Signal-Index-Pair Data has served in many executive and research positions including: Minister of
Preprocess Method to Enhance Precision," in IEEE International ICT, Student Deputy of Ministry of Science, Research and Technology,
Conference on Robotics and Automation (ICRA), 2010. Head of Iran Research Organization for Science and Technology, Head of
[5] Aylin Aksu, Joseph Kabara, and Michael B.Spring, "Reduction of Center for Advanced Electronics Research Center; and Technology
Location Estimation Error using Neural Networks," in Proceedings of Director for Space Systems in Iran Telecommunication Industries.
the first ACM international workshop on Mobile entity localization
and tracking in GPS-less environments,MELT'08 , 2008.
[6] C Laoudias et al., "Ubiquitous Terminal Assisted Positioning
Prototype," in IEEE Wireless Communications and Networking
Conference, WCNC , 2008.
[7] Hani Kaaniche and Farouk Kamoun, "Mobility Prediction in Wireless
Ad Hoc Networks Using Neural Networks," Journal of
Telecommunications, vol. 2, no. 1, 2010.
[8] Sherif Akoush and Ahmed Sameh, "Mobile User Movement
Prediction Using Bayesian Learning for Neural Networks," in
IWCMC '07 Proceedings of the 2007 international conference on
Wireless communications and mobile computing, 2007.
[9] J Amar Prathap Singh and M Karnan, "Intelligent location
management for UMTS networks using Fuzzy Neural Networks,"
Journal of Engineering and Technology Research, vol. 2, 2010.
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