Heuristic Algorithm for Optimal Design of the Two-Level Wireless ATM Network

ASIAIR > College of Computer Science > Department of Applied Informatics and Multimedia > Journal Article > Item 310904400/4725

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/4725

Title:	Heuristic Algorithm for Optimal Design of the Two-Level Wireless ATM Network
Authors:	D. R. Din;S. S. Tseng
Contributors:	Department of Information Science and Applications
Date:	2001-07
Issue Date:	2009-11-30 08:03:17 (UTC+0)
Publisher:	Asia University
Abstract:	In this paper, we investigate the optimal assignment problem of cells in PCS (Personal Communication Service) to switches in a wireless ATM network. Given cells and switches on an ATM network (whose locations are fixed and known), the problem is grouping cells into clusters and assigning these clusters to the switches in an optimum manner. This problem is modeled as a complex integer programming problem and finding an optimal solution of this problem is NP-complete. A three-phase heuristic algorithm MCMLCF (Maximum cell and minimum local communication first) consisting of Cell Pre-Partitioning Phase, Cell Exchanging Phase, and Cell Migrating Phase, is proposed. First, in the Cell Pre-Partitioning Phase, a three-step procedure (Clustering Step, Packing Step, and Assigning Step) is proposed to group cells into clusters. Second, Cell Exchanging Phase is proposed to greatly improve the result by repeatedly exchanging two cells in different switches. Finally, Cell Migrating Phase is proposed to reduce cost by repeatedly migrating all cells in a used switch to an empty switch. Experimental results indicate that the proposed algorithm runs efficiently. Comparing the results of the algorithm to a naive heuristic called NSF, we have shown that the computation time is reduced by 30.1%. Experimental results show that Cell Exchanging and Cell Migrating phases can reduce the total cost by 34.1% on average. By comparing the results of the proposed algorithm to the genetic algorithm, the heuristic method came close to optimum - on average within 5%. ? 2001 Institute of Information Science, Academia Sinica.
Relation:	Journal of Information Science and Engineering 17(4):647-666
Appears in Collections:	[Department of Applied Informatics and Multimedia] Journal Article

Files in This Item:

File	Description	Size	Format
		0Kb	Unknown	503	View/Open
310904400-4725.doc		38Kb	Microsoft Word	347	View/Open

Loading...