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Kevin P. Smyntek

dblp:125/1075 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1989
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Performance modeling and evaluation · 100%
Computer networks
1 paper
Physical-layer communications · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Performance modeling and evaluation › simulation › monte carlo simulation
importance sampling
0.011989
An experimental investigation of conventional and efficient importance sampling · IEEE Trans. Commun. 1989
Performance modeling and evaluation
simulation
0.011989
An experimental investigation of conventional and efficient importance sampling · IEEE Trans. Commun. 1989
Physical-layer communications › error probability analysis
bit error rate estimation
0.011989
An experimental investigation of conventional and efficient importance sampling · IEEE Trans. Commun. 1989

Methods — techniques the papers use, named apart from their topics

efficient importance sampling · 0.0conventional importance sampling · 0.0
YearPublicationVenuePosition
1989 An experimental investigation of conventional and efficient importance sampling
abstract
Importance sampling is a technique that can significantly reduce computer run-time in the estimation of bit error rate (BER). However, in the conventional implementation (CIS), the improvement reduces markedly for systems with long memory. An approach to recover the full improvement for such systems has been previously suggested, and is called 'efficient' important sampling (EIS). A report is presented on an extensive series of simulation-based experiments with CIS and EIS, both to compare theoretical predictions to experimental observations, as well as to gain insight into the conditions of applicability, especially for EIS.>
Michel C. Jeruchim, Peter M. Hahn, Kevin P. Smyntek, Robert T. Ray
IEEE Trans. Commun.3