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NCUEIR > College of Engineering > Department and Graduate Institute of Computer Science and Information Engineering > Periodical Articles > Item 987654321/9917

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/9917

Title:	Heuristic Algorithm for Optimal Design of Two-Level Wireless ATM Network
Authors:	Din, Der-Rong;Tseng, S. S.
Contributors:	資訊工程系
Keywords:	Wireless ATM;Design of algorithms;Assignment problem;Clustering problem;Graph partitioning problem;Personal communication services
Date:	2001-07
Issue Date:	2012-04-30T04:30:53Z
Publisher:	Institute of Information Science
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%.
Relation:	Journal of Information Science and Engineering, 17(4): 674-665
Appears in Collections:	[Department and Graduate Institute of Computer Science and Information Engineering] Periodical Articles

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