QuickSearch Abstract Format

Search History - Selected Records - My Profile - My Alerts

Abstract - Detailed

Record 55 from Inspec for: ((meybodi) WN All fields), 1790-2007

Check record to add to Selected Records

	55.	An adaptive limited fractional guard channel policy using continuous action learning automata Beigy, H. (Dept. of Comput. Eng., Amirkabir Univ. of Technol., Tehran, Iran); Meybodi, M.R. Source: 10th International Conference on Software, Telecommunications and Computer Networks. SoftCOM 2002, 2002, 536-40 ISBN: 953 6114 52 6 Conference: SOFTCOM 2002 - Conference on Software, Telecommunications and Computer Networks, 8-11 Oct. 2002 , Split, Croatia Sponsor: IEEE Commun. Soc. (COMSOC);Ministr. Sci. & Technol.;Republic of Croatia;Univ. Split Publisher: Univ. Split, Split, Croatia Abstract: In this paper, we first propose a new continuous action-learning automaton, which uses a stochastic estimator-learning algorithm to increase the speed of convergence. Then we introduce an adaptive and autonomous call admission algorithm, which uses the proposed learning automaton. The proposed algorithm minimizes blocking probability of new calls subject to constraint on dropping probability of handoff calls. The simulation results show that the performance of the proposed algorithm is close to the performance of limited fractional guard channel policy for which we need to know all traffic parameters in advance. Two advantages of the proposed policy are that it is fully autonomous and adaptive (12 refs.) Inspec controlled terms: convergence - learning automata - stochastic processes - telecommunication channels - telecommunication congestion control - telecommunication traffic Classification Code: B6150 Communication system theory - B0240Z Other topics in statistics - C3370 Control applications in telecommunications - C1230L Learning in AI - C4220 Automata theory - C1140Z Other topics in statistics Database: Inspec
		Full-text and Local Holdings Links