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Eduardo Mosca

dblp:272/5791 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1976
—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 · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Physical-layer communications › fading channels
fading dispersive channels
0.011976
Signal Design for Low-Error Probability in Fading Dispersive Channels · IEEE Trans. Commun. 1976
Physical-layer communications
signal design
0.011976
Signal Design for Low-Error Probability in Fading Dispersive Channels · IEEE Trans. Commun. 1976

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

error probability upper bound minimization · 0.0WSSUS channel modeling · 0.0
YearPublicationVenuePosition
1976 Signal Design for Low-Error Probability in Fading Dispersive Channels
abstract
The signal design problem for FSK communication via fading dispersive channels is considered. The channel is modeled as a linear filter whose time-varying impulse response is a sample function from a zero-mean Gaussian random field of arbitrary WSSUS type. The additive noise component in the received waveforms is supposed to be a zero-mean white Gaussian random process, and maximum likelihood demodulation is assumed. The signal design procedure here adopted consists of minimizing a known upper bound on the error probability, whereas the previous similar design method by Daly intended maximizing an upper bound on the detection probability for radar-astronomy targets. Though with slightly different optimal numerical values, here, as in Daly's problem, the signal design depends on a single parameter which is a simple functional of the channel timefrequency covariance function and of the signal envelope ambiguity function. A detailed example shows how the results of this concise paper can be used to optimize signal parameters and to predict the performance loss due to nonoptimal signal envelopes.
Ernesto Conte, Maurizio Longo, Eduardo Mosca
IEEE Trans. Commun.3