Automatic Detection of Wireless Transmissions

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.

HybNet: A Hybrid Deep Learning-Matched Filter Approach for IoT Signal Detection

“…ML methods, with a focus on DL, have been recently investigated in the field of wireless communications, including works on automatic modulation recognition (AMR) [19], [28], occupancy detection in the license-free band [29], [30], and optimization of interference management algorithms [31]. Many more applications of ML in communications exist, which are reviewed in [16] for the physical layer of communications, and in [32] for higher layers.…”

Self-supervised Learning for Clustering of Wireless Spectrum Activity

“…A pipeline for automatic detection of wireless transmissions is proposed in [12], along with software for manual labeling of transmissions in a spectrogram. A generic framework is designed that includes infrastructure for data acquisition, algorithms for data processing and evaluation.…”

“…As most applications in wireless spectrum management need to be aware of facts (i.e. type of technology, transmission parameters), developing a deep-learning based model to support such application typically requires labelled data that is expensive to acquire as it requires complex wireless and computing equipment [10], [11] or intense labelling efforts that do not always lead to high quality labels [12]. Semisupervised and active-learning are alternative techniques that have the advantage of using a relatively small amount of labeled samples for achieving performance that is comparable to the regular supervised approach.…”

Machine Learning Framework for Sensing and Modeling Interference in IoT Frequency Bands

“…Such methods methods assume that the receiver has prior knowledge of the signal location on the frequency-time plane. In order to encounter this constraint, manual labeling is utilized to identify the location and statistics of the frames in the spectrum prior to the application of the machine learning classifier [26]. Recall that the ISM-band is unlicensed, hence is unrestricted to any technology [27] making methods such as manual labeling inadequate to implement.…”