Peter J. Levine

Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.

? Tutorial for this page

Demo Data from EDBT 2026 / reviewers in the wild / expert

dblp:38/1666 · DBLP profile ↗

← Back

1ranked-venue papers

0as first author

0since 2021 · last 2001

—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 networks

1 paper

Physical-layer communications · 72% Network performance modeling · 28%

Topics — the 5 heaviest of 5, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Physical-layer communications channel coding and estimation	1	2001	Importance sampling for the random phase Gaussian channel · IEEE Trans. Commun. 2001
Physical-layer communications importance sampling	1	2001	Importance sampling for the random phase Gaussian channel · IEEE Trans. Commun. 2001
Network performance modeling › simulation modeling monte carlo simulation	1	2001	Importance sampling for the random phase Gaussian channel · IEEE Trans. Commun. 2001
Physical-layer communications › modulation m-ary signaling	1	2001	Importance sampling for the random phase Gaussian channel · IEEE Trans. Commun. 2001
Physical-layer communications modulation	1	2001	Importance sampling for the random phase Gaussian channel · IEEE Trans. Commun. 2001

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

variance reduction · 0.0importance sampling · 0.0

Year	Publication	Venue	Position
2001	Importance sampling for the random phase Gaussian channel abstract Importance sampling (IS) is developed as a variance reduction technique for Monte Carlo simulation of data communications over random phase additive white Gaussian noise channels. The binary problem (with known performance) is examined initially to determine parameter values and estimate the performance gain of IS. These results can then be applied to intractable m-ary signaling problems through composite IS. An example compares the performance of linear, square-law, and optimum receivers for binary block coded data. Peter F. Swaszek, Peter J. Levine	IEEE Trans. Commun.	2