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General Information
    • ISSN: 2010-3700
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus(since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(4): 415-418 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.156

Drift Analysis of Backlogged Packets in Slotted ALOHA

S. Patel and P. K. Gupta

Abstract—These Multiple access system (MAS) deals with a situation where multiple nodes (computers) are required to access commonly shared channel. These multiple users can be viewed as uncoordinated users or computers which compete to transmit messages in the form of packets or frames. It can be shown that the MAS is unstable due to nonlinearity of the problem in the form of contention of several nodes competing for the same channel. There are some well known algorithm to stabilize the throughput of slotted ALOHA i.e bayesian broadcast algorithm, splitting algorithm, modified stochastic gradient algorithm. In this paper, we have analyzed the number of backlogged packets by using statistical approach and stabilize the expected number of backlogged packets minus mean number of packets which are successfully transmitted.

Index Terms—Slotted ALOHA, throughput, backlogged packets, probabilistic distribution, diffusion approximation.

S. Patel is with the Jaypee Institute of Information Technology, Noida-201307, U. P. , India (e-mail: sanjeev.patel@jiit.ac.in).
P. K. Gupta is with the Jaypee University of Information Technology, Solan, 173234 H.P., India (e-mail: pradeep.gupta@juit.ac.in).


Cite: S. Patel and P. K. Gupta, "Drift Analysis of Backlogged Packets in Slotted ALOHA," International Journal of Machine Learning and Computing vol. 2, no. 4, pp. 415-418, 2012.

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