An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

Abstract

While deep learning (DL) technologies are now pervasive in state-of-the-art Computer Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications, a field loosely termed Radio Frequency Machine Learning (RFML). In particular, recent research has shown DL to be an enabling technology for Cognitive Radio (CR) applications as well as a useful tool for supplementing expertly defined algorithms for spectrum awareness applications such as signal detection, estimation, and classification. A major driver for the usage of RFML is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative raw Radio Frequency (RF) data to facilitate training and evaluation. However, in addition to this fundamental need for sufficient data, there are other key considerations, such as trust, security, and hardware requirements, that must be taken into account before deploying RFML systems in real-world wireless communication applications that largely go unaddressed in the current literature. This paper examines the prior works related to these major research considerations, with focus on the dependencies between them and factors unique to the RFML space.

Quantifying and Extrapolating Data Needs in Radio Frequency Machine Learning

“…In practice, the problem becomes vastly more complex as relative motion between transceivers, multiple transceivers, environmental noise, environmental motion, unintended radio emissions from manmade devices, mutlipath interference, and the imperfect hardware that transmits and receives the waveform are introduced. An introduction to the effects of imperfect hardware is given by Fettweis et al in [27] by looking at the individual degradations the hardware can add to a system as well as some mitigation strategies that can be applied; however, it is worth mentioning that these degradations are compounding and time varying, so while the worst of the effects can be calibrated out, the effects persist and cause a separation from the ideals assumed in (8). For an example of how these degradations affect the ideal, here the frequency independent In-phase and Quadrature Imbalance (IQI) of the transceivers result in a carrier modulation functions that are ideally expressed as ξ T X(ideal) (t, f c ) = exp(j2πf c t) for the transmitter for a carrier frequency f c and ξ RX(ideal) (t, f c ) = exp(−j2πf c t) for the receiver as…”

“…There are three common sources for data within ML dataset generation [8]. The first is the captured or collected data acquired by using a sensor and recording the data.…”

“…The process of creating an augmented dataset tries to bridge to strengths of captured and synthetic dataset creation, while covering their weaknesses [8]. By taking captured data and adding synthetic permutations of SNR, FO, and SRM the augmented dataset can smooth out missing observations from a limited collection event by having a better understanding of the detection characteristics of the sensors in use, while preserving all other real-world degradations native to the application space.…”

“…This paper focuses on providing an applied understanding of working with ML in the RF spectrum, with particular regard to understanding the training data in applications that fall in the domain space of Radio Frequency Machine Learning (RFML). To better clarify this, RFML is a sub- set of ML that overlaps communications, radar, or any other application space that utilizes the RF spectrum in a statistically repeatable manner, where ML algorithms are applied as intelligently close to the digitized samples of the RF spectrum, or Physical Layer in the Open Systems Interconnection (OSI) model, as possible [8]. For simplicity within this work, focus will be given to the field of Deep Learning (DL) as the particular subset within ML due to the well suited nature of DL systems at extracting inference from raw data [9].…”

Quantifying Raw RF Dataset Similarity for Transfer Learning Applications

“…The application of machine learning (ML) and deep learning (DL) techniques in wireless communications settings has yielded state-of-the-art spectrum awareness, cognitive radio (CR), and networking algorithms. Such algorithms that utilize raw radio frequency (RF) data as input to ML/DL techniques are considered radio frequency machine learning (RFML) algorithms [1], [2]. Like all traditional ML techniques, most state-of-the-art RFML algorithms require copious amounts of labelled training data drawn from the intended deployment environment, and for the intended deployment environment to remain stable, in order to achieve said state-of-the-art performance [3].…”