EDBT 2026 Demo / reviewers in the wild / expert
Magnus Malmström
dblp:262/8128
· DBLP profile ↗
3ranked-venue papers in the field
2as first author
3since 2021 · last 2025
0000-0003-0695-0720ORCID · corroborated
Domains — venue-derived; a paper can count in several
Other / Interdisciplinary · 3 (2 first)
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Long-Term Evolution-Based Time Synchronization in Distributed Sensor NetworksabstractThis paper investigates time synchronization in distributed sensor networks using the primary synchronization signal (PSS) in Long-Term Evolution (LTE). Two LTE-based time synchronization methods with receiver-to-receiver characteristics have been evaluated in simulations, the passive Scalable Wireless Network Synchronization (SWINS) and the active Reference Broadcast Synchronization (RBS). In addition, small scale hardware experiments were conducted for SWINS. Time synchronization is crucial for many applications, such as power grid monitoring, communication systems, and sensor data fusion. Global Navigation Satellite Systems (GNSS) are currently the state of the art for time synchronization in distributed wireless sensor networks. However, GNSS is vulnerable to jamming and spoofing, which requires alternative methods, e.g., using signals of opportunity. Both evaluated methods achieve accuracy comparable to GNSS in Matlab simulations with high SNR. SWINS performs better in synchronized LTE networks while RBS is superior in unsynchronized networks, which means that the LTE base station transmissions are not synchronous. The disturbance and sensitivity analysis indicates that joint clock offset and position estimation is preferable to sole clock offset estimation when the receiver position uncertainty exceeds 5 and 7 meters for SWINS and RBS respectively. The hardware experiments, using real experimental data, verify the simulation results by showing promising results and potential for real-world application. William Nordström, Magnus Malmström, Niclas Granström, Patrik Hedström, Ashwani Koul, Gustaf Hendeby |
FUSION | 2 |
| 2022 | Detection of outliers in classification by using quantified uncertainty in neural networks
Magnus Malmström, Isaac Skog, Daniel Axehill, Fredrik Gustafsson |
FUSION | 1 |
| 2021 | Modeling of the tire-road friction using neural networks including quantification of the prediction uncertainty
Magnus Malmström, Isaac Skog, Daniel Axehill, Fredrik Gustafsson |
FUSION | 1 |