Location Prediction Using Efficient Radial Basis Neural Network

+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.

+ Corresponding author. Tel.: +919439493937 ; fax: + 916742725113 E-mail address: saikath.bhattacharya@gmail.com

2011 International Conference on Information and Network Technology IACSIT Press, Singapore

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IPCSIT vol.4 (2011) © (2011)

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 networks use Gaussian activation function and are also feed forward network but have only 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)

where x=input, = width of the RBF unit or the scope of Gauss Function.

The output of the neural network is given by

(2)

where =weights of connection, =threshold of the output node

The cell id and time of visit is fed to the network for training. During the training the is used and during back-propagation of error its derivative is used to calculate the error[5,6,7]. For simulation 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: 69

xi) =

Ynet =

• 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 Learning steps Time (s) Radial basis network

Class A 50 0.650574 Class B 50 0.706216 Class C 50 0.685966

BP network Class A 2700 54.042024 Class B 5000 48.793200 Class C 5000 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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