Elevated Orthogonal Beacon Sensing for Hidden-Node Detection in Obstructed Wireless Networks: Concept, Temporal Analysis, and Deployment Estimate

This preprint investigates elevated passive observation as a complementary approach to hidden-node detection in obstructed wireless networks. Using NVIDIA Sionna ray-tracing simulations, it examines how observer height and propagation geometry affect the detection of transmitters that cause harmful interference at an intended receiver, distinguishing harmful candidates from confirmed non-harmful and unresolved cases. The study also explores temporal discovery reliability, observation-window requirements, and preliminary techno-economic trade-offs between alternative reporting architectures under synthetic outage conditions. Results indicate that elevated observation can improve harmful-transmitter detection in particular simulated configurations, but increased visibility does not necessarily translate into better interference classification. The work identifies key limitations and directions for further investigation, including cooperative sensing, refined spatial sampling, and validation against calibrated measurements.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-28
DOI
https://doi.org/10.5281/zenodo.23728794
Primary Topic
Cognitive Radio Networks and Spectrum Sensing
Type
preprint
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preprint

Elevated Orthogonal Beacon Sensing for Hidden-Node Detection in Obstructed Wireless Networks: Concept, Temporal Analysis, and Deployment Estimate

Rupsa Mandal Chakraborty
1 links
Zenodo (CERN European Organization for Nuclear Research)
Cognitive Radio Networks and Spectrum Sensing
preprint

Elevated Orthogonal Beacon Sensing for Hidden-Node Detection in Obstructed Wireless Networks: Concept, Temporal Analysis, and Deployment Estimate

Rupsa Mandal Chakraborty
preprint en

Abstract

This preprint investigates elevated passive observation as a complementary approach to hidden-node detection in obstructed wireless networks. Using NVIDIA Sionna ray-tracing simulations, it examines how observer height and propagation geometry affect the detection of transmitters that cause harmful interference at an intended receiver, distinguishing harmful candidates from confirmed non-harmful and unresolved cases. The study also explores temporal discovery reliability, observation-window requirements, and preliminary techno-economic trade-offs between alternative reporting architectures under synthetic outage conditions. Results indicate that elevated observation can improve harmful-transmitter detection in particular simulated configurations, but increased visibility does not necessarily translate into better interference classification. The work identifies key limitations and directions for further investigation, including cooperative sensing, refined spatial sampling, and validation against calibrated measurements.

Zenodo (CERN European Organization for Nuclear Research)
Cognitive Radio Networks and Spectrum Sensing
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