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Collecting data for Markov models of error patterns on data communications links
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Source ACM Southeast Regional Conference archive
Proceedings of the 30th annual Southeast regional conference table of contents
Raleigh, North Carolina
SESSION: Session 4A: Simulation table of contents
Pages: 409 - 412  
Year of Publication: 1992
ISBN:0-89791-506-2
Author
Wayne D. Smith  Mississippi State, MS
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Rome Laboratory is currently in the process of procuring Error Injector Units (EIU) to model the error behavior of data communications channels. A major component of this effort deals with finding Markov models that will simulate the behavior of these channels. This paper reports on the results of a Summer Faculty Research Fellowship to find Markov tables to load into the EIUs. A portion of the research involved searching current literature for Markov tables or error gap data. Alternative approaches to obtaining the desired Markov tables were also considered. This included substantial effort devoted to the design of a data collection experiment that could be used at Rome Laboratory to produce the needed data. This effort included the design of a microcomputer interface that would permit a microcomputer to monitor a "Fireberd" bit error rate tester and record the error statistics for analysis and conversion to a Markov table.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Gilbert, E. N., "Capacity of a Burst-Noise Channel," .Bell Systems Technical Journal, Vol 39, pp 1253-1266, Sept. 1960.
 
2
Fritzman, B. D., "A Binary Channel Characterization Using Partitioned Markov Chains," IEEE Transactions on Information Theory, Vol. IT-13, pp 221-227, April 1967.
 
3
Tsai, S., "Markov Characterization of the HF Channel," IEEE Transactions on Communications Technology, Vol Corn-17, pp 24-32, Feb. 1969.

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