ORAN-Sense: Localizing Non-cooperative Transmitters with Spectrum Sensing and 5G O-RAN
Yago Lizarribar, Roberto Calvo-Palomino, Alessio Scalingi, Giuseppe Santaromita, Gérôme Bovet, Domenico Giustiniano
Abstract
Crowdsensing networks for the sole purpose of performing spectrum measurements have resulted in prior initiatives that have failed primarily due to their costs for maintenance. In this paper, we take a different view and propose ORAN-Sense, a novel architecture of Internet of Things (IoT) spectrum crowd-sensing devices integrated into the Next Generation of cellular networks. We use this framework to extend the capabilities of 5G networks and localize a transmitter that does not collaborate in the process of positioning. While 5G signals can not be applied to this scenario as the transmitter does not participate in the localization process through dedicated pilot symbols and data, we show how to use Time Difference of Arrival-based positioning using low-cost spectrum sensors, minimizing hardware impairments of low-cost spectrum receivers, introducing methods to address errors caused by over-the-air signal propagation, and proposing a low-cost synchronization technique. We have deployed our localization network in two major cities in Europe. Our experimental results indicate that signal localization of non-collaborative transmitters is feasible even using low-cost radio receivers with median accuracies of tens of meters with just a few sensors spanning cities, which makes it suitable for its integration in the Next Generation of cellular networks.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext bd25a643-697a-444a-89ba-a59f7747fc9cBuilds on2
- A Framework for Wireless Technology Classification using Crowdsensing PlatformsAlessio Scalingi, Domenico Giustiniano, Roberto Calvo-Palomino, Nikolaos Apostolakis et al.INFOCOM 2023 · 9 citations
- Robust indoor localization with ADS-BAlexander Canals, Pascal Josephy, Simon Tanner, Roger WattenhoferMobiCom 2021 · 8 citations
Related papers
- Enabling IoT Self-Localization Using Ambient 5G SignalsSuraj Jog, Junfeng Guan, Sohrab Madani, Ruochen Lu et al.NSDI 2022
- Large Network UWB Localization: Algorithms and ImplementationNakul Garg, Irtaza Shahid, Ramanujan K. Sheshadri, Karthikeyan Sundaresan et al.NSDI 2025 · 3 citations
- TAPS: Three-Dimensional Amplitude-Phase-Spatial IQ CompressionThanos Triantafyllou, Qingrui Pan, Mahesh K. MarinaINFOCOM 2026
- Seirios: leveraging multiple channels for LoRaWAN indoor and outdoor localizationJun Liu, Jiayao Gao, Sanjay K. Jha, Wen HuMobiCom 2021 · 50 citations
- Det-RAN: Data-Driven Cross-Layer Real-Time Attack Detection in 5G Open RANsAlessio Scalingi, Salvatore D'Oro, Francesco Restuccia, Tommaso Melodia et al.INFOCOM 2024 · 19 citations
