2011 International Conference on Information and Network Technology
IPCSIT vol.4 (2011) © (2011) IACSIT Press, Singapore
Location Prediction Using Efficient Radial Basis Neural Network
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Saikath Bhattacharya1 and Sudhansu Sekhar Singh1
1
School Of Electronics Engineering,
KIIT University,Bhubaneswar ,Orissa
Abstract: In real life mobile users shows a non random movement behavior. On a daily basis the user
travels in a well defined area and only occasionally it travels to new area. This feature can be extracted and
used to predict the next location of the user, which can reduce a lot of cost incurred by the network in
searching a user. In this present paper for prediction of user’s location, movement based model is prepared
and with the support of neural network user’s path and location is tracked in an optimal way. Specifically an
efficient Radial Basis Function network (RBF) is used to predict the result and the performance is compared
with Back Propagation (BP) model.
Keywords: Location Management, Location Updating, Prediction, Neural Network, Paging.
1. Introduction
Location management deals with different strategies of searching a user accurately. It can be divided into
two operations: location updating and location prediction. In location updating the MSC continuously
records the updates send by the mobile user, whenever it crosses some threshold boundaries while moving
towards a new location. One of the key features of cellular communication is to forward a call to a user
within a stipulated amount of time. In finding a user, network uses certain resources to trace out the exact
location. The resources can be the bandwidth, memory of the database and any form of energy which is
consumed during the operation of locating the user. But with fixed bandwidth and limited resources, it
becomes very difficult to forward a call to the user. Therefore if the MSC can predict the user’s next location
he is going to visit, the MSC can directly forward the call to that location area without wasting any additional
resources.
2. Location Management:
Location management deals with developing strategies which can reduce the cost of finding the user in
wireless network. There are two basic objectives of location management:
1.) When should a mobile user update its location? 2) How should a network search or page the user. So
that the total cost incurred by the network is minimum [1, 3]. Traditionally in present wireless
communication, each base station broadcast a unique location id on the control channels. The mobile device
listen to this broadcast and whenever he finds that he has entered a new location area he updates or notifies
the MSC about his new location [2]. The MSC maintains a profile of the user location id .Now if a call
arrives on the mobile phone the MSC looks for the users last updated location id and forward the call to that
base station. This sometimes results to call dropping as the MSC sometime doesn’t have the record of user’s
last location he has visited.
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Corresponding author. Tel.: +919439493937 ; fax: + 916742725113
E-mail address: saikath.bhattacharya@gmail.com
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3. Structure of RBF Neural Network
Neural network is an excellent structure for capturing, training and predicting the user’s next location
[4]. In 1985 Powell proposed Radial Basis function method for exact interpolation. Radial basis function
and are also feed forward network but have only
networks use Gaussian activation function
one hidden layer. Therefore RBFN consists of three layers input, hidden and output layer as shown in fig:
Fig 1: A RBF structure
Designing a radial basis network often takes much less time than training a feed-forward network, and
can sometimes result in fewer neurons used for training. The output of im unit vi(xi) in the hidden layer is
given by:
(1)
xi) =
where x=input, = width of the RBF unit or the scope of Gauss Function.
The output of the neural network is given by
(2)
Ynet =
where
=weights of connection,
=threshold of the output node
is used and
The cell id and time of visit is fed to the network for training. During the training the
is used to calculate the error[5,6,7]. For simulation
during back-propagation of error its derivative
we have used newbr( ) and newff( ) for creating a 3 layer structure and plot.pref() for plotting the mean error
in matlab .
4. Classification of Users
A user is free to move in the network as a result randomness increases in his movement and it becomes
difficult to track the user. Users can be classified depending on the amount of randomness they incur on
their movement [8].
•
•
•
Class A: These types of users are highly predictable. They daily follow the same path everyday and
hardly there is a deviation in their path.
Class B: These types of users are highly random. Each day they follow a different path.
Class C: These types of users represent a real life user as they follow a certain path on weekends or
once in week or month goes to new location.
5. Mobility Pattern and Data Training:
For predicting the path the base station id were used and a user’s movement profile is being made for
data preparation. The following assumptions were taken:
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•
•
•
The user updates his location periodically each after unit intervals
For each individual subscriber neural network is trained individually.
The user’s movement was collected for 3 days i.e. 50 data set and during the training the cell id and
the corresponding time of visit is feed into the network.
6. Result and Analysis:
Results were simulated and the following prediction pattern was observed for radial base neural network
and feed- forward neural network. One of the most important factors while training a neural network is to
take care of over- learning. We have assumed the learning goal of 0.001 and a learning rate of 0.2 while
training. The number of hidden layers was 15.
Table 1: Results during Training
Network
Radial
network
BP network
basis
Learning steps
Class A
Class B
Class C
50
50
50
Time (s)
0.650574
0.706216
0.685966
Class A
Class B
Class C
2700
5000
5000
54.042024
48.793200
49.286949
Fig 2: Learning error of RBF
Fig 3: Mean square error of BP network while training
From fig 2 and 3 the RBF converges towards the goal but the BP network tries to converge but it
oscillates near the goal [9, 10].
Fig 4 Class C type user’s location prediction using RBF
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Fig 5: Class C user’s location prediction using BP
From the above figure BP fails to follow the previous location areas whereas RBF network can easily
follow the path the user travel after it visits a new location area .
Fig 6: Class A user results using RBF
Fig 7: Class A prediction results
Fig 8: class B training with BP
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Fig: 9: class B training using
Fig 6 and 7 shows the result of RBF and BP which are very identical as the user follows a well defined
pattern. During complete randomness in the user’s movement the results of training the network under RBF
and BP could not learn the path of the user.
7. Conclusion:
Class B users which are highly random BP learning error fails to converge whereas the RBF can predict.
For class C users which depict a real life scenario RBF converged well even during training and testing of
the data but BPNN failed even the randomness came in users movement. Class C user’s showed a near
realistic approach for a user’s movement and RBF understood the underlying relations better than the BP. In
future Mobile assisted prediction can be done where a smart phone can use neural network in his background
application for creating a users and send the results to the Mobile Switching Centre.
8. Reference
[1]. I F Akyildiz And J Sm Ho , “A Mobile User Location Update and Paging Mechanism Under Delay Constraints”
in Proc.ACM- SIGCOMM '95, 1995, Pages: 244-255
[2]. Garry J Mullet,” Introduction to Wireless Telecommunication Systems And Networks”. Delmar Cengage 2006 ,
Pg 105
[3]. Bar-Noy A. ; Kessler, I. ; Sidi, M “Mobile User: To update or not to update”. ; IEEE/Proc INFOCOM '94, 12-16
Jun 1994, pg: 570
[4]. Joe Capka ,Raouf Bautaba ,Mobility Prediction In Wireless Networks Using Neural Networks ,pg 320-333
[5]. S N Sinandam S Sumathi S N Deepa ,Introduction To Neural Networks Using Matlab 6.0 ,Tata Mcgraw Hill
Companies , chapter 8 pg 184
[6]. Martin T Hagman ,Howard B Demuth, Mark Beale ,Neural Network Design, Pws Pub., 1996
[7]. Neural Networks: A Comprehensive Foundation Simon S. Haykin, Macmillan, 1994, pg 54
[8]. B.P Vijay Kumar ,P Venkataram , “Prediction Based Location Management Using Multilayer Neural
Networks,” Journal, Indian Institute Of Science, pg 7-21
[9]. Fenglian Liu “An Improved Rbf Network For Predicting Location In Mobile Network ”,IEEE/Conf, International
Conference On Natural Computation 2009, pg 345-348
[10]. Preadeep Bilurkar ,Narasimhq Rao, Gowri Krishna , Ravi Jain, Application Of Neural Network Techniques For
Location Prediction In Mobile Networking ,IEEE/CONFERENCE, ICONIP 2002 ,pg 2157-2161
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