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Luca Sardellitti

dblp:348/7166 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
0009-0003-7728-4881ORCID · reported

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

Computer networks · 1 · 1 first-author · 1 since 2021

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 · 61% Internet of things and sensor networks · 39%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Physical-layer communications › modulation
constellation design
0.912025
Optimal Binary Signaling for a Two Sensor Gaussian MAC Network · IEEE Trans. Commun. 2025
Internet of things and sensor networks › wireless sensor network › distributed algorithms for sensor networks
distributed detection
0.912025
Optimal Binary Signaling for a Two Sensor Gaussian MAC Network · IEEE Trans. Commun. 2025
Physical-layer communications › multiple access
multiple access channel
0.912025
Optimal Binary Signaling for a Two Sensor Gaussian MAC Network · IEEE Trans. Commun. 2025
Internet of things and sensor networks
wireless sensor network
0.312025
Optimal Binary Signaling for a Two Sensor Gaussian MAC Network · IEEE Trans. Commun. 2025

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

optimization · 0.9
YearPublicationVenuePosition
2025 Optimal Binary Signaling for a Two Sensor Gaussian MAC Network
abstract
We consider a two sensor distributed detection system transmitting a binary non-uniform source over a Gaussian multiple access channel (MAC). We model the network via binary sensors whose outputs are generated by binary symmetric channels of different noise levels. We prove an optimal one dimensional constellation design under individual sensor power constraints which minimizes the error probability of detecting the source. Three distinct cases arise for this optimization based on the parameters in the problem setup. In the most notable case (Case III), the optimal signaling design is to not necessarily use all of the power allocated to the more noisy sensor (with less correlation to the source). We compare the error performance of the optimal one dimensional constellation to orthogonal signaling. The results show that the optimal one dimensional constellation achieves lower error probability than using orthogonal channels.
Luca Sardellitti, Glen Takahara, Fady Alajaji
IEEE Trans. Commun.1