Algorithm design methods for radio communications systems are poised to undergo a massive disruption over the next several years. Today, such algorithms are typically designed manually using compact analytic problem models. However, they are shifting increasingly to machine learning based methods using approximate models with high degrees of freedom, jointly optimized over multiple subsystems, and using real-world data to drive design which may have no simple compact probabilistic analytic form.
Over the past five years, this change has already begun occurring at a rapid pace in several fields. Computer vision tasks led deep learning, demonstrating that low level features and entire end-to-end systems could be learned directly from complex imagery datasets, when a powerful collection of optimization methods, regularization methods, architecture strategies, and efficient implementations were used to train large models with high degrees of freedom.
Within this work, we demonstrate that this same class of end-to-end deep neural network based learning can be adapted effectively for physical layer radio systems in order to optimize for sensing, estimation, and waveform synthesis systems to achieve state of the art levels of performance in numerous applications.
First, we discuss the background and fundamental tools used, then discuss effective strategies and approaches to model design and optimization. Finally, we explore a series of applications across estimation, sensing, and waveform synthesis where we apply this approach to reformulate classical problems and illustrate the value and impact this approach can have on several key radio algorithm design problems. / Ph. D. / Radio communications and sensing systems are used pervasively in the modern world every day life to connect phones, computers, smart devices, industrial devices, internet services, space systems, emergency and military users, radar systems, interference monitoring systems, defense electronic systems, and others. Optimizing these systems to function together reliably and efficently in an ever more complex world is becoming increasingly hard and impractical.
Our work introduces a new and radically different method for the design of radio systems by casting them in a new way as artificial intelligence problems relying on the field of machine learning called deep learning to find and optimize their design. We detail and demonstrate the first such deep learning based communciations and sensing systems operating on raw radio signals and quantify their performance when compared to existing methods, showing them to be competitive with and in some cases significantly better performing than state of the art systems today.
These ideas, and the evidence of their viability, are central to the emerging field of machine learning communications systems, and will help to make tomorrow’s wireless systems faster, cheaper, more reliable, more adaptive, more efficient, and lower power than currently possible. In a world of ever increasing complexity and connectedness, this new approach to wireless system design from data using machine learning offers a powerful new strategy to improve systems by directly leveraging the complexity in real world data and experience to find efficiencies where current day approaches and insufficient simplified models and design tools can not.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/89649 |
| Date | 06 December 2017 |
| Creators | O'Shea, Timothy James |
| Contributors | Electrical Engineering, Clancy, Thomas Charles III, McGwier, Robert W., Reed, Jeffrey H., Ramakrishnan, Naren, Raman, Sanjay |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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