Doctor of Philosophy (PhD), Wright State University, 2019, Electrical Engineering

Signal detection, parameter estimation and modulation classification are widely applied to many areas and plays a very important role in civilian and military, such as bio-science, criminal psychology, communication engineering, radar system, electronic warfare and so on. In the civilian field, with the increasing number of wireless electronic devices and higher transmission data rate demand, the problem of spectrum congestion becomes more and more highlighted and urgent. In recent years, wireless industry has shown great interest in Cognitive Radio (CR) and Dynamic Spectrum Access (DSA) network, whose primary function is to use limited frequency bands to transmit own signals without any interference with other primary users. Hence, the accuracy of signal detection and parameters estimation are particularly important and can provide reliable communication performance for cognitive radio users. In the military field, electronic warfare is crucial important part in modern war, such as own signal needs to be hidden, securely transmitted and received, enemy's signals need to be identified, located and jammed. Thus, in such a non-cooperative environment, signal detection, parameter estimation and modulation classification technologies become more and more important and challenging. In the past few decades, several signal detection methods have been proposed, such as energy-based detection, matched filter-based detection and cyclostationary feature based detection. Energy based detection is simple to implement, but poorly performing at low SNR. Although the matched filter-based detection is the optimal detector, it needs to accurately know the prior information of the detected signal. Hence, matched filter-based detection is impractical to implement in real environment, such as non-cooperative environment. Cyclostationary feature based signal detection has high computational complexity, but it can be used for high-precision signal detection in low SNR environments. In rec (open full item for complete abstract)

Committee: Zhiqiang Wu Ph.D. (Advisor); Vasu Chakravarthy Ph.D. (Committee Member); Saiyu Ren Ph.D. (Committee Member); Yan Zhuang Ph.D. (Committee Member); Xiaodong Zhang Ph.D. (Committee Member) Subjects: Electrical Engineering

Master of Science, University of Toledo, 2018, Electrical Engineering

Software defined radio (SDR) systems have attracted much attention recently for their affordability and simplicity for hands-on experimentation. They can be used for implementation of dynamic spectrum allocation (DSA) algorithms in cognitive radio (CR) platform. There has been a massive research in the DSA algorithms both in machine learning and signal processing paradigm, but, these CRs are still incapable to decide which algorithm suites for specific scenario. A comparison between the spectrum sensing algorithms using machine learning techniques and statistical signal processing techniques is needed in order to know which algorithm suits best for resource constrained environments for CRs and spectrum observatories. Two challenges; namely, multi-transmitter detection and automatic modulation classification (AMC) are chosen. Novel machine learning based and statistical signal processing based multi-transmitter detection algorithm are proposed and used in the comparison. After comparing accuracy, for multi-transmitter detection, machine learning algorithm has accuracy of 70% and 80% for 2 and 5 user system, respectively, whereas, the accuracy for statistical signal processing algorithm is 50% for 2 and 5 user system. For AMC, both signal processing and machine learning algorithm have a perfect accuracy beyond 10 dB for 100 test samples (64-QAM being an exception) but for 1000 test samples, the machine learning algorithm outperforms the signal processing algorithm. Time comparison showed that signal processing algorithms, in both cases, take fraction of the time required by machine learning algorithms. Hence, it is recommended to use machine learning techniques where accuracy is important and use signal processing approach where timing is important. The process of selecting the algorithms can be regarded as a tradeoff between accuracy and time.

Committee: Vijay Devabhaktuni (Committee Chair); Harshavardan Chenji (Committee Co-Chair); Ahmad Javaid (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy (PhD), Wright State University, 2008, Engineering PhD

Recent studies have suggested that spectrum congestion is mainly due to the inefficient use of spectrum rather than its unavailability. Dynamic Spectrum Access (DSA) and Cognitive Radio (CR) are two terminologies which are used in the context of improved spectrum efficiency and usage. The DSA concept has been around for quite some time while the advent of CR has created a paradigm shift in wireless communications and instigated a change in FCC policy towards spectrum regulations. DSA can be broadly categorized as using a 1) Dynamic Exclusive Use Model, 2) Spectrum Commons or Open sharing model or 3) Hierarchical Access model. The hierarchical access model envisions primary licensed bands, to be opened up for secondary users, while inducing a minimum acceptable interference to primary users. Spectrum overlay and spectrum underlay technologies fall within the hierarchical model, and allow primary and secondary users to coexist while improving spectrum efficiency. Spectrum overlay in conjunction with the present CR model considers only the unused (white) spectral regions while in spectrum underlay the underused (gray) spectral regions are utilized. The underlay approach is similar to ultra wide band (UWB) and spread spectrum (SS) techniques utilize much wider spectrum and operate below the noise floor of primary users.Software defined radio (SDR) is considered a key CR enabling technology. Spectrally modulated, Spectrally encoded (SMSE) multi-carrier signals such as Orthogonal Frequency Domain Multiplexing (OFDM) and Multi-carrier Code Division Multiple Access (MCCDMA) are hailed as candidate CR waveforms. The SMSE structure supports and is well-suited for SDR based CR applications. This work began by developing a general soft decision (SD) CR framework, based on a previously developed SMSE framework that combines benefits of both the overlay and underlay techniques to improve spectrum efficiency and maximizing the channel capacity. The resultant SD-SMSE framework prov (open full item for complete abstract)

Committee: Arnab Shaw PhD (Committee Co-Chair); Zhiqiang Wu PhD (Committee Co-Chair); Fred Garber PhD (Committee Member); Michael Temple PhD (Committee Member); Michael Bryant PhD (Committee Member) Subjects: Electrical Engineering