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Cellular Wireless Network Design and

Processing with Grid Cluster Presented By Pakorn Leesutthipornchai

King Mongkut’s University of Technology Thonburi

TGCC2007 Presentation

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Contents

Background or Related Work

1

Proposed Work

2

Results

3

Conclusion

4

5

Introduction

Introduction

Cellular wireless networks have become essential to mobile users that need communication services regardless of time, location, and mobility pattern.

Introduction

MSC –Mobile Switching CenterBSC –Base Station Controller BS –Base station

BS

BS BS

BS

PSTN/Transmission Network

BS

BS BS

BS

BSCBSC

MSC

MSC

Figure 1. Typical wireless network architecture.

Background or Related Work

The Network service providers have to dedicate a substantial amount of their resources to do the network planning, install, and maintain the network equipments, such as BTS, BSC and MSC, network link and link interface.

Cost effective network design becomes a necessity.

Literature Reviews

In 2003, Pomerleau Yanick et al [1] proposed a constraint programming model for cellular network design and solved with a local search heuristic called “Constraint Programming Local Search (CPLS)”.

Proposed Work

Network Design Problem & Model Formulation

Brute Force Search Processing with Grid Cluster

Network Design Problem and Model

Objective:

Equation 1

where CL is the cost of the link and interface cards, and CN is the cost of the allocated BSCs and MSCs.

Constraints : Bandwidth, Network Flow, Node Capacity, and Link Capacity.

),(),,(min,,,,

yxCzwvC NLzyxwv

Network Design Problem and Model

BTS

BSC

MSC

In 100 (km)2

Figure 2 Cellular wireless backhaul network design problem

Each BTS communicates with other BTSs in a communication demand units (Erlang).

BTS, BSC, and MSC have their own locations (in this research we generate them randomly)

Network Design Problem and Model

From Figure 2, the location and type (A, B, or C) of the Base Stations are already obtained.

Under interface, link, and capacity constraints of each BSC :

We select the BSCs type (A, B, or C) that gives the lowest cost.

We select the interfaces (DS-1) and links (for DS-1) of BTSs-BSC that gives the lowest cost.

Network Design Problem and Model

Also under interface, link, and capacity constraints of each MSC :

We select the MSCs type (A, B, or C) that gives the lowest cost.

We select the interfaces (DS-1, or DS-3) and links (for DS-1, or DS-3) of BSCs-MSC that gives the lowest cost.

BTS(A)BTS(A) BTS(C)BTS(C)BTS(A) BTS(A)BTS(A)BTS(B)

PSTN

Select Interfaces & Links

Select Interfaces & Links

MSC(A) MSC(B)

Select MSC Type

BSC(B) BSC(A) BSC(A) BSC(A) BSC(C)

Select BSC Type

Network Design Problem and Model

High performance computer uses less computation time. The objective problem in this research is a complicated

problem then the high performance computer helps to reduce a computation time.

A heuristic method uses less computation time than brute force search. However a heuristic result needs to compare with the brute force search result to ensure that the heuristic approach is effective.

Brute Force Search Processing

Brute Force Search Processing

Brute Force Search is an algorithm which finds the best solution from all possible solutions.

All the possible solutions are explored and the best solution is obtained, the solution from Brute Force Search can guarantee that the brute force result is the best solution.

High performance computer is required to run a Brute Force Search algorithm because it is compute-intensive.

Grid Cluster at CPE, KMUTT Xeon 2.8 GHz 10 dual-core processors with 5

nodes Memory 20 GB Storage 0.4 TB Gigabits LAN

Brute Force Search Processing

Brute Force Search Processing

MSC

BSC BSC BSC BSC

MSC

MSC

BSC BSC BSC BSC

MSC

MSC

BSC BSC BSC BSC

MSC

Encoding String

1 1 1 1 1 1 1 1 : 1 -1 -1 -1

1 1 1 1 1 1 1 1 : 2 -1 -1 -1

2 1 1 1 1 1 1 1 : 1 1 -1 -1 2 1 1 1 1 1 1 1 : 2 1 -1 -1

…

4 4 4 4 4 4 4 4 : -1 -1 -1 1

4 4 4 4 4 4 4 4 : -1 -1 -1 2

Brute Force Search Processing

Search space :

(#BSC)(#BTS)(#MSC)(#BSC)

if network size= 22 BTS – 9 BSC – 5 MSC

the search space = =

= number of possible solutions

271092.1 922 59

Experiment

Goal : finding the best solution

Workload : the network size : 8 network sizes.

Configuration :

The call blocking rate is 2% : 1 call blocking rate.

network locations are randomly generated with a uniform distribution in a square region of 100 kilometers. The demand between each pair of BTSs was generated randomly in the interval (0,0.2] erlang with a uniform distribution : 3 sets of location and communication demand.

Result

Problem Size(BTS_BSC_MSC) Network Cost

Time(DD:HH:MM:SS)

5_3_2 647,041.34 00:00:00:00

8_4_2 781,400.05 00:00:00:09

10_4_2 1,018,200.83 00:00:03:24

12_4_2 1,104,158.98 00:01:22:55

22_9_5 1,448,594.96 00:23:16:23

25_20_10 1,557,714.40 08:03:12:13

30_15_8 1,938,028.95 08:06:35:29

50_20_10<326147, ,

828. >16:01:57:32

Table 1 Cost and Computation time of Brute Force Search Algorithm

“>16:01:57:32” means brute force search use computation time longer than 16 days 01 hour 57 minutes 32 seconds, Thus the cost is less than 3,261,478.28

Conclusion

From the experiment, Brute Force Search algorithm requires to use a high performance computer.

Since cellular wireless network design problem is a complicated problem. Practically, a “Heuristic” method which uses less computation time is applied to solve this problem.

However a heuristic result needs to compare with the brute force search result to ensure that the heuristic approach is effective.

Other Works

Network design with link restoration with GA, Brute Force Search Network design with path restoration with GA

MSC

BSC BSC

Figure 3 Cellular wireless network design with link restoration

Future Work

In this research we try to use a parallel computing to reduce the computation time from a search space.

The parallel computing method, huge used in this research is dividing the search space in to n-processors with master-slave criteria.

D0

D1

D2

D3

4 Proc

D0

D1

D2

D3

Proc0

Proc2

Proc1

Proc3

Figure 4 Parallel Computing Method

Q&A

Any Suggestion or Question?

References

[1] Y. Pomerleau, S. Chamberland and G. Pesant, “A constraint programming approach for the design problem of cellular wireless networks”, IEEE CCECE 2003 Canadian Conference, Vol. 2, pp 881 – 884, 2003.

[2] M. Martinez-Diaz, J. Fierrez-Aguilar, F. Alonso-Fernandez, J. Ortega-Garcia and J.A. Siguenza, “Hill-Climbing and Brute-Force Attacks on Biometric Systems: A Case Study in Match-on-Card Fingerprint Verification”, Carnahan Conferences Security Technology, Proceedings 2006 40th Annual IEEE International, pp 151 – 159, 2006.

[3] N. Couture and K.B. Kent, “The effectiveness of brute force attacks on RC4”, Communication Networks and Services Research, Proceedings 2004 Second Annual Conference, pp 333 – 336, 2004.

[4] A.W.K. Kong, D. Zhang and M. Kamel, “Analysis of Brute-Force Break-Ins of a Palmprint Authentication System”, IEEE Transactions, Vol. 36, Issue 5, pp 1201 – 1205, 2006.

[5] P. Leesutthipornchai, N. Wattanapongsakorn and C. Charnsripinyo, “Cellular wireless network design with genetic algorithm”, Proceedings of the 2007 ECTI Conference, Mae Fah Luang University, Chiang Rai, Thailand, pp 1163-1166, 2007.

[6] J. Veerasamy and S. Venkatesant, “Effect of traffic splitting on link and path restoration planning”, IEEE Transactions, pp 1867 – 1871, 1994.

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