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Learning from Data in Radio Algorithm Design Learning from Data in Radio Algorithm Design Timothy James O’Shea Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical Engineering T. Charles Clancy Robert W. McGwier Narendran Ramakrishnan Sanjay Raman Jeffrey Reed Oct 26th, 2017 Arlington, Virginia Keywords: deep learning, radio, physical layer, software radio, machine learning, neural networks, sensing, communications system design, modulation, coding, sensing Copyright 2017, Timothy James O’Shea Learning from Data in Radio Algorithm Design Timothy James O’Shea ABSTRACT Algorithm design methods for radio communications systems are poised to undergo a massive disruption over the next several years. Today, such algorithms are typically de- signed 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, architec- ture 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 se- ries 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. Learning from Data in Radio Algorithm Design Timothy James O’Shea GENERAL AUDIENCE ABSTRACT Radio communications and sensing systems are used pervasively in the modern world every day life to connect phones, computers, smart devices, industrial devices, inter- net 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 sys- tems 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 op- erating 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 ma- chine learning communications systems, and will help to make tomorrow’s wireless sys- tems 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 power- ful 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. Acknowledgments Thank you to all my current and former colleagues at Virgina Tech, NC State, Bell Labs, the US Government, the GNU Radio Community and industry who supported, critiqued, mentored, collaborated, co-authored and discussed countless ideas surrounding software radio, cognitive radio, and deep learning, especially my advisor Charles Clancy, who has been a constant source of support and inspiration, and has provided me with significant freedom to explore new and disruptive ideas. I am also very grateful to the individuals and organizations who have supported myself and my work throughout my studies including VT, DeepSig, DARPA, NSF, DOD, LM, Hawkeye360, Federated Wireless and others who made much of this possible. iv Dedication This work is dedicated to my family, friends, colleagues, mentors, sponsors and research inspirations, all of whom have supported me and contributed to this work in countless immeasurable ways for which I am extremely grateful. More abstractly, this work is dedicated to engineering as a creative discipline. While many engineering fields have become complex and tedious, end-to-end learning based approaches to design offer to relieve some of the tedium and slow progress surrounding the field today. It is my sincere hope that the future of engineering will become more of a creative outlet for experimentalists, contrarians, pragmatists and makers. That the expansion of machine learning will empower all people to create and to view engineering in a positive, fun, and creative light and artform, accessable to all rather than as the obscure, slow moving, and specialized field that it can sometimes seem today. v Contents 1 Introduction 1 1.1 Chasing Optimality in Communication System Design . .3 1.2 Neural Networks in Radio System Design . .4 1.3 Implications, Trends and Challenges in Deep Learning . .6 1.4 Deep Cognitive Radio Systems . .8 2 Background 10 2.1 Radio Signal Processing . 11 2.1.1 Digital Communications . 12 2.1.2 Radio Channel Models . 15 2.2 Cognitive Radio . 21 2.2.1 Sensing Techniques . 22 vi 2.2.2 Control Modeling . 23 2.3 Deep Learning Models . 24 2.3.1 Error Feedback and Objectives . 24 2.3.2 Network Model Primitives . 29 2.3.3 Regularization . 34 2.3.4 Architectural Strategies . 37 2.3.5 High Performance Computing . 39 2.3.6 Model Search . 44 2.3.7 Model Introspection . 45 3 Learning to Communicate 50 3.1 The Channel Autoencoder . 52 3.2 Learning to Synchronize with Attention . 65 3.3 Multi-User Interference Channel . 71 3.4 Learning Multi-Antenna Diversity Channels . 77 3.5 Learning MIMO with CSI Feedback . 81 3.6 System Identification Over the Air . 87 vii 4 Learning to Label the Radio Spectrum 89 4.1 Learning Estimators from Data . 91 4.2 Learning to Identify Modulation Types . 99 4.2.1 Expert Features for Modulation Recognition (Baseline) . 101 4.2.2 Time series Modulation Classification With CNNs . 103 4.2.3 Deep Residual Network Time-series Modulation Classification . 108 4.3 Learning to Identify Radio Protocols . 136 4.4 Learning to Detect Signals . 141 5 Learning Radio Structure 150 5.1 Unsupervised Structure Learning . 151 5.2 Unsupervised Class Discovery . 155 5.3 Neural Network Model Discovery and Optimization . 159 6 Conclusion 164 6.1 Publication List . 167 Bibliography 172 viii List of acronyms ACF auto-correlation function ADC analog-to-digital converter AE autoencoder AI artificial intelligence AM amplitude modulation ANN artificial neural network ARP address resolution protocol AWGN additive white Gaussian noise BCE binary cross-entropy BER bit error rate BLER block error rate ix BPSK binary phase shift keying CAF cross ambiguity function CCE categorical cross-entropy CFO carrier frequency offset CNN convolutional neural network CQI channel quality information CR cognitive radio CSI channel state information CUDA Compute Unified Device Architecture DL deep learning DAC digital to analog converter DNN deep neural network DNS domain name server DOF degrees of freedom DSA dynamic spectrum access DSP digital signal processing x DTree decision tree EM electromagnetic FEC forward error correction FFT fast Fourier transform FLOPS floating point operations per second FM frequency modulation FSK frequency shift keying FV Fisher Vector GF galois field GR GNU Radio GRU gated recurrent unit GPGPU general purpose graphic processing unit GPU graphic processing unit GMR ground mobile radio HMM hidden Markov model HOC higher order cumulants xi HOS higher order statistic HOM higher order moment I/Q In-phase and Quadrature ICA independent component analysis IEEE Institute of Electrical and Electronics Engineers IID independent and identically distributed IOU Intersection over union ISM industrial, scientific, and medical radio ISI inter-symbol interference LDPC low density parity check LO local oscillator LOS line of sight LTE long term evolution LSTM long short-term memory LTI linear time invariant MAE mean absolute error xii MAP maximum a posteriori MF matched filter MFCC Mel-frequency cepstral coefficient MIMO multiple-input multiple-output ML machine learning MLD maximum likelihood MLE maximum likelihood estimation MLSP machine learning for signal processing MMSE minimum mean square error MNIST Modified National Institute of Standards and Technology MRSA mean-response scaled initializations MU multi-user NNSP neural networks for signal processing MSE mean squared error NLP natural languasge processing NN neural network xiii OFDM orthogonal
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