Automatic Detection of Wireless Transmissions

Timotej Gale,Rares-Andrei Mosoi,Carolina Fortuna,Tomaz Solc,Mihael Mohorcic

doi:10.1109/access.2020.2970840

Abstract

The current understanding of activity in the wireless spectrum is limited to mostly punctual studies of aggregated energy values. However, there is a need and increasing technological means for a better understanding of spectrum usage by automatically detecting and recognizing wireless transmissions in an unlicensed or shared frequency band. In this paper we propose, implement and evaluate a framework for automatic detection of wireless transmissions. Our framework includes a manual component as our assessment suggests manual labor has a paramount impact on tuning and maintaining good performance of an automatic transmission detection system. However, a considerable problem in this aspect is represented by the disagreement amongst human annotations which is a universally recognized issue. To this end, we discuss and evaluate challenges in generating labeled datasets that can then be used as ground truth for evaluating and possibly training automatic transmission detection systems. We also propose two methods for automatic transmission detection that are not based on machine learning and therefore do not need training data and evaluate their performance against each other and manually labeled data. Our results show that generating human-labeled ground truth data is an expensive and imperfect process. Humans on average require 90 minutes to label 56 minutes of unlicensed European narrowband spectrum. The experts that generate the ground truth sometimes only agree on as little as 40.18% of the labeled cases.

Highlights

The increased penetration of data-driven knowledge technologies, which are complementary to the existing analytical approaches, is already changing the modern information society
We show that 1) generating human-labeled data is an expensive and imperfect process but necessary as 2) the performance of an automatic detection system depends on the available ground truth data
AUTOMATIC TRANSMISSION DETECTION FRAMEWORK we propose a framework that can be used for automatic detection of transmissions in wireless spectrum

Summary

Introduction

The increased penetration of data-driven knowledge technologies, which are complementary to the existing analytical approaches, is already changing the modern information society. In wireless networks, devices were foreseen to use the information provided by the spectrum sensing algorithms for dynamic spectrum access [1]. Spectrum sensing algorithms and low-cost hardware enabled conducting long term spectrum usage studies around the world [2]. Such studies generated additional knowledge, on a larger scale than previously possible. Broadband multi-GHz real-time spectrum analytics enables fast. The associate editor coordinating the review of this manuscript and approving it for publication was Ding Xu. generation of information by guiding the sensing devices [3]. Real-time wideband spectrum sensing systems able to monitor a larger portion of the spectrum are being proposed [4]

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Conclusion