arXiv subjects

Alan J. Michaels

Publications and source records attributed to Alan J. Michaels.

6 recordsLinked to original sources

arXiv2024

Training from Zero: Radio Frequency Machine Learning Data Quantity Forecasting

The data used during training in any given application space is directly tied to the performance of the system once deployed. While there are many other factors that go into producing high performance models within machine learning, there is no doubt that the data used to train a system provides the foundation from which to build. One of the underlying rule of thumb heuristics used within the machine learning space is that more data leads to better models, but there is no easy answer for the question, "How much data is needed?" This work examines a modulation classification problem in the Radio Frequency domain space, attempting to answer the question of how much training data is required to achieve a desired level of performance, but the procedure readily applies to classification problems across modalities. The ultimate goal is determining an approach that requires the least amount of data collection to better inform a more thorough collection effort to achieve the desired performance metric. While this approach will require an initial dataset that is germane to the problem space to act as a \textit{target} dataset on which metrics are extracted, the goal is to allow for the initial data to be orders of magnitude smaller than what is required for delivering a system that achieves the desired performance. An additional benefit of the techniques presented here is that the quality of different datasets can be numerically evaluated and tied together with the quantity of data, and ultimately, the performance of the architecture in the problem domain.

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arXiv2022

An Analysis of RF Transfer Learning Behavior Using Synthetic Data

Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but have yet to be fully utilized in the field of radio frequency machine learning (RFML). This work systematically evaluates how radio frequency (RF) TL behavior by examining how the training domain and task, characterized by the transmitter/receiver hardware and channel environment, impact RF TL performance for an example automatic modulation classification (AMC) use-case. Through exhaustive experimentation using carefully curated synthetic datasets with varying signal types, signal-to-noise ratios (SNRs), and frequency offsets (FOs), generalized conclusions are drawn regarding how best to use RF TL techniques for domain adaptation and sequential learning. Consistent with trends identified in other modalities, results show that RF TL performance is highly dependent on the similarity between the source and target domains/tasks. Results also discuss the impacts of channel environment, hardware variations, and domain/task difficulty on RF TL performance, and compare RF TL performance using head re-training and model fine-tuning methods.

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arXiv2022

Assessing the Value of Transfer Learning Metrics for RF Domain Adaptation

The use of transfer learning (TL) techniques has become common practice in fields such as computer vision (CV) and natural language processing (NLP). Leveraging prior knowledge gained from data with different distributions, TL offers higher performance and reduced training time, but has yet to be fully utilized in applications of machine learning (ML) and deep learning (DL) techniques to applications related to wireless communications, a field loosely termed radio frequency machine learning (RFML). This work begins this examination by evaluating the how radio frequency (RF) domain changes encourage or prevent the transfer of features learned by convolutional neural network (CNN)-based automatic modulation classifiers. Additionally, we examine existing transferability metrics, Log Expected Empirical Prediction (LEEP) and Logarithm of Maximum Evidence (LogME), as a means to both select source models for RF domain adaptation and predict post-transfer accuracy without further training.

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arXiv2021

Training Data Augmentation for Deep Learning Radio Frequency Systems

Applications of machine learning are subject to three major components that contribute to the final performance metrics. Within the category of neural networks, and deep learning specifically, the first two are the architecture for the model being trained and the training approach used. This work focuses on the third component, the data used during training. The primary questions that arise are ``what is in the data'' and ``what within the data matters?'' Looking into the Radio Frequency Machine Learning (RFML) field of Automatic Modulation Classification (AMC) as an example of a tool used for situational awareness, the use of synthetic, captured, and augmented data are examined and compared to provide insights about the quantity and quality of the available data necessary to achieve desired performance levels. There are three questions discussed within this work: (1) how useful a synthetically trained system is expected to be when deployed without considering the environment within the synthesis, (2) how can augmentation be leveraged within the RFML domain, and lastly, (3) what impact knowledge of degradations to the signal caused by the transmission channel contributes to the performance of a system. In general, the examined data types each have useful contributions to a final application, but captured data germane to the intended use case will always provide more significant information and enable the greatest performance. Despite the benefit of captured data, the difficulties and costs that arise from live collection often make the quantity of data needed to achieve peak performance impractical. This paper helps quantify the balance between real and synthetic data, offering concrete examples where training data is parametrically varied in size and source.

cs.LG↗

arXiv2020

The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications

While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications. In particular, recent research has shown deep machine learning to be an enabling technology for cognitive radio applications as well as a useful tool for supplementing expertly defined algorithms for spectrum sensing applications such as signal detection, estimation, and classification (termed here as Radio Frequency Machine Learning, or RFML). A major driver for the usage of deep machine learning in the context of wireless communications is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative 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/software issues, that must be taken into account before deploying deep machine learning systems in real-world wireless communication applications. This paper provides an overview and survey of prior work related to these major research considerations. In particular, we present their unique considerations in the RFML application space, which are not generally present in the image, audio, and/or text application spaces.

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arXiv2018

Emitter Identification Using CNN IQ Imbalance Estimators

Specific Emitter Identification is the association of a received signal to a unique emitter, and is made possible by the naturally occurring and unintentional characteristics an emitter imparts onto each transmission, known as its radio frequency fingerprint. This work presents an approach for identifying emitters using Convolutional Neural Networks to estimate the IQ imbalance parameters of each emitter, using only raw IQ data as input. Because an emitter's IQ imbalance parameters will not change as it changes modulation schemes, the proposed approach has the ability to track emitters, even as they change modulation scheme. The performance of the developed approach is evaluated using simulated quadrature amplitude modulation and phase-shift keying signals, and the impact of signal-to-noise ratio, imbalance value, and modulation scheme are considered. Further, the developed approach is shown to outperform a comparable feature-based approach, while making fewer assumptions and using less data.

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