# Radio Access Technology Characterisation Through Object Detection

Source: [Radio Access Technology Characterisation Through Object Detection](<https://www.sota2.com/research/paper/68fa68061e209131cea19983>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:27:00.192Z

Authors: Erika Fonseca, Joao F. Santos, Francisco Paisana, Luiz A. DaSilva

Published: 2020-07-27T13:40:58.000Z

## Abstract

\\ac{RAT} classification and monitoring are essential for efficient coexistence of different communication systems in shared spectrum. Shared spectrum, including operation in license-exempt bands, is envisioned in the \\ac{5G} standards (e.g., 3GPP Rel. 16). In this paper, we propose a \\ac{ML} approach to characterise the spectrum utilisation and facilitate the dynamic access to it. Recent advances in \\acp{CNN} enable us to perform waveform classification by processing spectrograms as images. In contrast to other \\ac{ML} methods that can only provide the class of the monitored \\acp{RAT}, the solution we propose can recognise not only different \\acp{RAT} in shared spectrum, but also identify critical parameters such as inter-frame duration, frame duration, centre frequency, and signal bandwidth by using object detection and a feature extraction module to extract features from spectrograms. We have implemented and evaluated our solution using a dataset of commercial transmissions, as well as in a \\ac{SDR} testbed environment. The scenario evaluated was the coexistence of WiFi and LTE transmissions in shared spectrum. Our results show that our approach has an accuracy of 96\\% in the classification of \\acp{RAT} from a dataset that captures transmissions of regular user communications. It also shows that the extracted features can be precise within a margin of 2\\%, %of the size of the image, and is capable of detect above 94\\% of objects under a broad range of transmission power levels and interference conditions.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2007.13561>)

[PDF](<https://arxiv.org/pdf/2007.13561.pdf>)