VLDB 2026 Research / reviewers in the wild / expert
Emmanuelle Gautherat
dblp:72/1640
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1998
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 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 · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Physical-layer communications › equalization
blind deconvolution |
0.0 | 1 | 1998 | Identification of Noisy Linear Systems with Discrete Random Input · IEEE Trans. Inf. Theory 1998 |
Methods — techniques the papers use, named apart from their topics
hankel matrix characterization · 0.0consistent estimation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1998 | Identification of Noisy Linear Systems with Discrete Random InputabstractWe propose a new method for the blind deconvolution of a discrete linear system perturbed with additive noise. The method comes from a characterization of discrete variables when perturbed with additive noise with unknown variance together with a characterization of this variance through Hankel matrix. Based on this probabilistic description, an estimator is proposed for the inverse system and the variance of the noise. These estimators are shown to be consistent under weak assumptions, whatever the signal-to-noise ratio is. In particular, the input signal needs not be independently distributed. Numerical examples demonstrate the effectiveness of the method, even when nonstationary signals are used as inputs. Elisabeth Gassiat, Emmanuelle Gautherat |
IEEE Trans. Inf. Theory | 2 |