arXiv subjects

William H. Clark IV

Publications and source records attributed to William H. Clark IV.

5 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.

cs.LG↗

arXiv2024

Automatic Modulation Classification using a Waveform Signature

Cognitive Radios (CRs) build upon Software Defined Radios (SDRs) to allow for autonomous reconfiguration of communication architectures. In recent years, CRs have been identified as an enabler for Dynamic Spectrum Access (DSA) applications in which secondary users opportunistically share licensed spectrum. A major challenge for DSA is accurately characterizing the spectral environment, which requires blind signal classification. Existing work in this area has focused on simplistic channel models; however, more challenging fading channels (e.g., frequency selective fading channels) cause existing methods to be computationally complex or insufficient. This paper develops a novel blind modulation classification algorithm, which uses a set of higher order statistics to overcome these challenges. The set of statistics forms a signature, which can either be used directly for classification or can be processed using big data analytical techniques, such as principle component analysis (PCA), to learn the environment. The algorithm is tested in simulation on both flat fading and selective fading channel models. Results of this blind classification algorithm are shown to improve upon those which use single value higher order statistical methods.

eess.SP↗

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

When is Enough Enough? "Just Enough" Decision Making with Recurrent Neural Networks for Radio Frequency Machine Learning

Prior work has demonstrated that recurrent neural network architectures show promising improvements over other machine learning architectures when processing temporally correlated inputs, such as wireless communication signals. Additionally, recurrent neural networks typically process data on a sequential basis, enabling the potential for near real-time results. In this work, we investigate the novel usage of "just enough" decision making metrics for making decisions during inference based on a variable number of input symbols. Since some signals are more complex than others, due to channel conditions, transmitter/receiver effects, etc., being able to dynamically utilize just enough of the received symbols to make a reliable decision allows for more efficient decision making in applications such as electronic warfare and dynamic spectrum sharing. To demonstrate the validity of this concept, four approaches to making "just enough" decisions are considered in this work and each are analyzed for their applicability to wireless communication machine learning applications.

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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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