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Signal Detection and Digital Modulation Classification-Based Signal Detection and Digital Modulation Classification-Based Spectrum Sensing for Cognitive Radio A Dissertation Presented by Curtis M. Watson to The Department of Electrical and Computer Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the field of Computer Engineering Northeastern University Boston, Massachusetts September 2013 Curtis Watson is employed by The MITRE Corporation at 202 Burlington Road, Bedford, MA 01730. The author's affiliation with The MITRE Corporation is provided for identification pur- poses only, and is not intended to convey or imply MITRE's concurrence with, or support for, the positions, opinions or viewpoints expressed by that author. Approved for Public Release; Distri- bution Unlimited Case Number 13-2957. c Curtis M. Watson 2013; All Rights Reserved NORTHEASTERN UNIVERSITY Graduate School of Engineering Dissertation Title: Signal Detection and Digital Modulation Classification-Based Spectrum Sensing for Cognitive Radio Author: Curtis M. Watson Department: Electrical and Computer Engineering Approved for Dissertation Requirement for the Doctor of Philosophy Degree Dissertation Adviser: Prof. Waleed Meleis Date Dissertation Reader: Prof. Jennifer Dy Date Dissertation Reader: Prof. Kaushik Chowdhury Date Department Chair: Prof. Sheila Hemami Date Graduate School Notified of Acceptance: Director of the Graduate School: Prof. Sara Wadia Fascetti Date Acknowledgments I want to thank the MITRE Corporation for the opportunity to pursue my Ph.D. while I am employed as a part of the Accelerated Graduate Degree Program (AGDP), as well as the encour- agement and support by my management. I could not imagine a successful completion of my research and dissertation without the benefits provided by AGDP. I appreciate the constant encouragement by my supervisors, and in particular I want to thank Jerry Shapiro and Kevin Mauck who continually checked on my progress and motivated me to complete my dissertation. I also want to thank Kevin Burke for his encouragement to complete my dissertation. Additionally, I enjoyed the technical discussions about digital communications, communication systems, and machine-learning & classification theory, which provided useful guidance and thoughts to complete my research. I want to thank Matt Keffalas for many hours of discussion about machine learning and how it can be applied to communications, in particular digital modulation classification. Matt planted many seeds of ideas that helped to shape my thoughts that were applied to my research. I want to thank Prof. Jennifer Dy and Prof. Kaushik Chowdhury for being on my committee. I appreciate the questions, suggestions, and critiques they provided which led to improvements in my research. I want to thank Prof. Waleed Meleis for advising me through the completion of my research and dissertation. His advice helped me navigate this unique experience from which I am a better research investigator and a more polished writer. I am grateful for taking his Combinatorial Optimization course not only because it is an interesting subject, but also because it led me to seek him out to be my advisor. Finally, I want to thank from the bottom of my heart my wife, Jaclyn for helping me complete this journey. She helped by watching the kids when I needed to do work on my research or to be on campus. She gave up weekends for me to complete my dissertation. Jaclyn has been with me from the start to the finish of my Ph.D. studies and she will enjoy with me the fruits of this labor now that it is complete. Thank you Jaclyn and I love you. Abstract Spectrum sensing is the process of identifying available spectrum channels for use by a cognitive radio. In many cases, a portion of the spectrum is licensed to a primary communication system, for which the users have priority access. However, many studies have shown that the licensed spectrum is vastly underutilized, which presents an opportunity for a cognitive radio to access this spectrum, and motivates the need to research spectrum sensing. In this dissertation, we describe a spectrum sensing architecture that characterizes the carrier frequency and bandwidth of all narrowband signals present in the spectrum, along with the modulation type of those signals that are located within a licensed portion of the spectrum. From this radio identification, a cognitive radio can better determine an opportunity to access the spectrum while avoiding primary users. We describe a narrowband signal detection algorithm that takes an iterative approach to jointly estimate the carrier frequency and bandwidth of individual narrowband signals contained within a received wideband signal. Our algorithm has a number of tunable parameters, and the algorithm gives consistent performance as we varied these parameter values. Our algorithm outperforms the expected performance of an energy detection algorithm, in particular at lower signal-to-noise ratio (SNR) values. These behavioral features make our algorithm a good choice for use in our spectrum sensing architecture. We describe a novel constellation-based digital modulation classification algorithm that uses a fea- ture set that exploits the knowledge about how a noisy signal should behave given the structure of the constellation set used to transmit information. Our algorithm's classification accuracy outper- forms a set of literature comparisons' results by an average increase of 9.8 percentage points, where the most dramatic improvement occurred at 0 dB SNR with our accuracy at 98.9% compared to 37.5% for the literature. The classifier accuracy improves using our feature set compared to the classifiers’ accuracy using two feature set choices that are common in the literature by an average increase of 13.35 and 5.31 percentage points. These qualities make our algorithm well-suited for our spectrum sensing architecture. Finally, we describe our spectrum sensing architecture that coordinates the execution of our nar- rowband signal detection and modulation classification algorithms to produce an spectrum activity report for a cognitive radio. This report partitions the spectrum into equally-sized cells and gives an activity state for each cell. Our architecture detects spectrum opportunities with a probability of 99:4% compared to 87:7% and 93:8% for two other comparison approaches that use less infor- mation about the primary user's waveform. Our architecture detects \grey-space" opportunities with a probability of 96:1% compared to 49:1%. Also, the false alarm rate is significantly lower for our architecture, 13:3% compared to 46:9% and 62:7% for the two comparisons. Consequently, we conclude that a cognitive radio can achieve better spectrum utilization by using our spectrum sensing architecture that is aware of the primary user(s) waveform characteristics. vi Contents 1 Introduction 1 1.1 Statement of Work and Research Importance . .3 1.2 Related Work . .3 1.3 Contributions of this Research . .6 1.4 Dissertation Outline . .8 2 Background 10 2.1 Digital Communications . 10 2.1.1 The Transmitter . 12 2.1.2 The Channel . 13 2.1.3 The Receiver . 14 2.1.4 Problem Space Under Consideration . 15 2.2 Pattern Recognition . 16 2.2.1 Classification Theory . 17 2.2.2 Binary Class Label Classifiers . 18 2.2.2.1 Support Vector Machine Description . 18 2.2.3 Multiple Class Label Classifiers . 23 3 Narrowband Signal Detection Algorithm 25 3.1 Model . 25 3.1.1 Log-Likelihood Equation Derivation . 27 3.2 Signal Detection Algorithm . 29 3.2.1 Subroutine: Find-New-Signal( V, S ) ..................... 32 3.2.2 Subroutine: Adjust-Parameters( V, S ) ................... 33 3.2.3 Subroutine: Merge-Signals( S ) ........................ 34 3.2.4 Subroutine: Finish?( V, S ) ........................... 34 3.3 Update Method Derivation . 34 vii 3.3.1 Amplitude Update . 35 3.3.2 NSPS Update . 37 3.3.3 Offset Update . 39 3.4 Experiments and Analysis . 42 3.4.1 Initial Experiments . 42 3.4.2 Parameter Refinement Experiments . 45 3.4.3 Comparison to the Energy Detection Algorithm . 50 3.5 Discussion . 59 4 Modulation Classification 61 4.1 Literature Review . 62 4.2 Complex Baseband Model . 67 4.3 The EM Algorithm . 69 4.3.1 EM Algorithm: E-step . 70 4.3.2 EM Algorithm: M-step . 71 4.4 Classification Process . 72 4.4.1 Feature Vector Description . 73 4.4.2 Weight Vector Training . 78 4.4.2.1 Chromosome Mutation . 79 4.4.2.2 Chromosome Mating . 79 4.4.2.3 Population Growth . 79 4.4.2.4 Population Fitness Evaluation . 80 4.4.2.5 Population Reduction . 83 4.5 Experiments . 85 4.5.1 First Evaluation { Our Algorithm Only . 85 4.5.1.1 Evaluation Results . 88 4.5.2 Second Evaluation { Examination of our Algorithm with Respect to the Lit- erature . 92 4.5.2.1 Classification Learning Approach for this Evaluation . 93 4.5.2.2 Experiments for this Evaluation . 94 4.5.3 Third Evaluation { Examination of our Feature Set with Respect to the Literature . 97 4.5.3.1 Classification Learning Approach for this Evaluation . 98 4.5.3.2 Experiments for this Evaluation . 99 4.6 Discussion and Future Improvements . 101 viii 5 Spectrum Sensing Architecture 103 5.1 Architecture Description . 104 5.1.1 Spectrum Sensing Problem Statement and Assumptions . 104 5.1.2 Primary User Knowledge Base . 106 5.1.3 Narrowband Signal Detection . 107 5.1.4 Channelization and Modulation Classification for Primary User Identification 108 5.1.5 Spectrum Activity Reports . 108 5.2 Spectrum Sensing Evaluation . 111 5.2.1 Evaluation Metrics . 112 5.2.2 Directed Test Evaluation . 119 5.2.3 Random Test Evaluation . 129 5.3 Discussion and Future Work . 134 6 Summary 137 A Formal Proof on Decision Exactness of Error-Correcting Output Code Frame- work and One-Versus-All and One-Versus-One Ensemble Classifiers 156 A.1 Introduction . 156 A.2 ECOC Framework Proofs .
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