Cellular Wireless Network Design and Processing with Grid Cluster

1. TGCC2007 Presentation การประมวลผล การออกแบบเครือข่ายโทรศัพท์เคลื่อนที่ไร้สาย โดยใช้กริดคลัสเตอร์ Cellular Wireless Network Design and Processing with Grid Cluster Presented By Pakorn Leesutthipornchai King Mongkut’s University of Technology Thonburi

2. Contents 1 Introduction 2 Background or Related Work 3 Proposed Work 4 Results 5 Conclusion

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

4. MSC –Mobile Switching Center BSC –Base Station Controller BS –Base station PSTN/ Transmission Network MSC MSC BSC BSC Introduction Figure 1. Typical wireless network architecture.

5. 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.

6. 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)”.

7. Proposed Work • Network Design Problem & Model Formulation • Brute Force Search Processing with Grid Cluster

8. 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.

9. Network Design Problem and Model BTS, BSC, and MSC have their own locations (in this research we generate them randomly) Each BTS communicates with other BTSs in a communication demand units (Erlang). In 100 (km)2 BTS BSC MSC Figure 2 Cellular wireless backhaul network design problem

10. 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.

11. 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.

12. PSTN Select MSC Type MSC(A) MSC(B) Select Interfaces & Links Select BSC Type BSC(B) BSC(A) BSC(A) BSC(A) BSC(C) Select Interfaces & Links BTS(B) BTS(A) BTS(A) BTS(A) BTS(A) BTS(C) BTS(C) Network Design Problem and Model BTS(A)

13. Brute Force Search Processing • 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.

14. 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.

15. Brute Force Search Processing • 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

16. MSC MSC MSC MSC MSC MSC BSC BSC BSC BSC BSC BSC BSC BSC BSC BSC BSC BSC Brute Force Search Processing

17. 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

18. 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.

19. Result 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

20. 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.

21. MSC BSC BSC Other Works • Network design with link restoration with GA, Brute Force Search • Network design with path restoration with GA Figure 3 Cellular wireless network design with link restoration

22. 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 Proc0 D0 D1 Proc1 D1 4 Proc D2 D2 Proc2 D3 D3 Proc3 Figure 4 Parallel Computing Method

23. Q&A • Any Suggestion or Question?

24. 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 200640th 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.

25. Thank You ! .