EDBT 2026 Demo / reviewers in the wild / expert
Omer Altug Sevimay
dblp:427/2768
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0001-5518-7023ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 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 |
Wireless sensing and localization · 33% Cellular and mobile networks · 33% Internet architecture and protocols · 33% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cellular and mobile networks
5g |
1.0 | 1 | 2026 | Digital Network Twin-Enabled Synchronization and Localization · IEEE J. Sel. Areas Commun. 2026 |
Wireless sensing and localization
cellular localization |
1.0 | 1 | 2026 | Digital Network Twin-Enabled Synchronization and Localization · IEEE J. Sel. Areas Commun. 2026 |
Internet architecture and protocols
network synchronization |
1.0 | 1 | 2026 | Digital Network Twin-Enabled Synchronization and Localization · IEEE J. Sel. Areas Commun. 2026 |
Methods — techniques the papers use, named apart from their topics
ray tracing · 1.0digital network twin · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Digital Network Twin-Enabled Synchronization and LocalizationabstractThis paper addresses the challenge of achieving simultaneous synchronization and localization of all the active terminals within a cellular network from only one Base Station (BS). We propose a novel approach leveraging Digital Network Twins (DNT), which integrates these two critical tasks within a unified framework.We begin by analyzing User Equipment (UE)-to-network time synchronization, both theoretically and through experimental validation using a 5th generation (5G) testbed, identifying it as the primary obstacle to accurate localization. Then, to address this challenge, we introduce a DNT-based framework that leverages high-fidelity ray-tracing simulations on a 3D digital replica of the environment. This enables precise UE-to-network alignment, dynamic environmental mapping, and accurate real-time localization starting from one Next Generation Node Base (gNB). The proposed method integrates Angle Delay Channel Power Matrix (ADCPM) characterization and Time of Flight (ToF) data with the DNT prior knowledge of the environment, eliminating the need for network cooperation or prior on-field channel measurements for precise localization. We first validate the proposed approach through an outdoor measurement campaign and then demonstrate its effectiveness via numerical simulations, compared to existing localization techniques in scenarios where only a single gNB is available. The method achieves on average a positioning accuracy of less than 6m in the static case and 8m in the dynamic scenario, using a ray-tracing granularity that is not excessively fine (4 × 4 m), even under worst-case synchronization and Non-Line of Sight (NLoS) conditions. Niccolò Paglierani, Francesco Linsalata, Omer Altug Sevimay, Lorenzo Cazzella, Damiano Badini, Maurizio Magarini, Umberto Spagnolini |
IEEE J. Sel. Areas Commun. | 3 |