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Davide Bosetto

dblp:62/2298 · DBLP profile ↗
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2ranked-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 · 1Graphics, computer vision, multimedia, augmented reality and games · 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 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Physical-layer communications › synchronization
carrier frequency estimation
0.012001
Progressive refinement approach to MLE: an application to carrier frequency recovery · IEEE Trans. Commun. 2001
Physical-layer communications › channel estimation
maximum likelihood estimation
0.012001
Progressive refinement approach to MLE: an application to carrier frequency recovery · IEEE Trans. Commun. 2001
Physical-layer communications
synchronization
0.012001
Progressive refinement approach to MLE: an application to carrier frequency recovery · IEEE Trans. Commun. 2001

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

progressive refinement · 0.0monte carlo simulation · 0.0
YearPublicationVenuePosition
2001 Progressive refinement approach to MLE: an application to carrier frequency recovery
abstract
Carrier frequency estimation plays an important role in digital communication receivers. A progressive refinement approach to maximum-likelihood estimation is proposed as a means to improve the performance of a suboptimal estimation algorithm and achieve different tradeoffs among complexity, accuracy and dynamic range. As an example, an application is described for carrier frequency recovery in burst-mode digital transmissions. The Luise and Reggiannini (1995) synchronization algorithm is employed as the master frequency estimator. The performance of the algorithm is demonstrated by means of a simulation study, assuming quadrature phase-shift keying modulation; the results show that the progressive refinement approach can be usefully exploited, for example to improve the estimation range of the LR algorithm, without losing the accuracy and the computational complexity.
Gabriella Olmo, Letizia Lo Presti, Davide Bosetto
IEEE Trans. Commun.3
2000 Turbo estimation algorithms: general principles, and applications to modal analysis
Letizia Lo Presti, Gabriella Olmo, Davide Bosetto
Signal Process.3