VLDB 2026 Research / reviewers in the wild / expert
Luca Sardellitti
dblp:348/7166
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Physical-layer communications › modulation
constellation design |
0.9 | 1 | 2025 | 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 |
0.9 | 1 | 2025 | Optimal Binary Signaling for a Two Sensor Gaussian MAC Network · IEEE Trans. Commun. 2025 |
Physical-layer communications › multiple access
multiple access channel |
0.9 | 1 | 2025 | Optimal Binary Signaling for a Two Sensor Gaussian MAC Network · IEEE Trans. Commun. 2025 |
Internet of things and sensor networks
wireless sensor network |
0.3 | 1 | 2025 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Optimal Binary Signaling for a Two Sensor Gaussian MAC NetworkabstractWe 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 |