Master of Science in Engineering (MSEgr), Wright State University, 2007, Electrical Engineering

GNU Radio is an open source software-defined radio project, and the Universal Software Radio Peripheral (USRP) is hardware designed specifically for use with GNU Radio. Together, these two technologies have been used to implement very sophisticated, yet low cost, software-defined radios. Since software-defined radio and software-defined radar are really one in the same technologies, it stands to reason that GNU Radio and the USRP could be utilized to form a low cost radar sensor. In this thesis, we discuss the design of a prototype software-defined radar, built using the open source GNU Radio and open specification USRP projects. The prototype design is introduced, followed by the results of laboratory testing. A discussion on the expected operational performance of the prototype is then provided. The thesis concludes with the development and analysis of a waveform optimization algorithm that is capable of improving signal to interference plus noise ratio in the presence of a band-limited interferer. The low computational complexity of this algorithm make it amenable to software-defined radar.

Committee: Brian Rigling (Advisor) Subjects:

Master of Science in Engineering, Youngstown State University, 2009, Department of Electrical and Computer Engineering

The purpose of this work is to design and implement a low cost and flexible Software Defined Radio platform. Software Defined Radio is an emerging technology that gives engineers and scientists the ability to create reconfigurable wireless technology. Functions that were traditionally performed in hardware are implemented in software, thus making a Software Defined Radio reconfigurable without requiring hardware modifications. Current Software Defined Radio designs are usually based on a personal computer or Field Programmable Gate Array and offer either flexibility at a high cost of implementation or a low cost of implementation that sacrifices flexibility. In this work, a Software Defined Radio platform is presented which offers flexibility with a low cost of implementation. This is achieved by using a personal computer-based architecture with Universal Serial Bus interface to the analog-to-digital conversion and Radio Frequency modules. This external hardware interface, constructed from off the shelf components, provides the versatility for the proposed Software Defined Radio platform at minimal cost. A proof-of-concept design is then implemented and tested, which demonstrates the feasibility of the design.

Committee: Faramarz Mossayebi PhD (Advisor); Frank X. Li PhD (Committee Member); Jalal Jalali PhD (Committee Member) Subjects: Electrical Engineering

Master of Science in Electrical Engineering (MSEE), Wright State University, 2023, Electrical Engineering

A method for software-defined radio array calibration is presented. The method implements a matched filter approach to calculate the phase shift between channels. The temporal stability of the system and calibration coefficients are shown through the standard deviation over the course of four weeks. The standard deviation of the phase correction was shown to be less than 2 deg. for most channels in the array and within 8 deg. for the most extreme case. The standard deviation in amplitude scaling was calculated to be less than 0.06 for all channels in the array. The performance of the calibration is evaluated by the antenna gain and the difference from the ideal beam shape for the peak side lobe level and first null depth. For one example data collection, the gain was 61 dB for the array with a maximum difference of 0.2246 dB for the peak side lobe level and 0.3998 dB for the first null depth.

Committee: Michael A. Saville Ph.D. (Advisor); Zhiqiang Wu Ph.D. (Committee Member); Josh Ash Ph.D. (Committee Member) Subjects: Electrical Engineering

Master of Science, The Ohio State University, 2023, Electrical and Computer Engineering

Over the past several decades, vehicle manufacturers have been increasingly adding technological improvements to the vehicles they release to the public. While these advancements are well-intentioned, especially in regards to vehicular safety and security feature upgrades, they have dramatically increased the cyber attack surface for malicious actors. These attackers are taking advantage of software-defined radios to assist in their methods. In this thesis, we aim to improve upon a well-known vehicular replay attack, the roll-jam attack, as well as develop an machine learning-assisted algorithm to allow a target vehicle to detect if it is being jammed. The traditional vehicular roll-jam attack is an effective means to gain access to the target vehicle by jamming and recording key fob inputs from a victim. However, it requires specifc knowledge of the attack surface, and delicate tuning of software-defned radio parameters. We have developed an enhanced version of the roll-jam attack that uses a known noise signal for jamming, in contrast to the additive white Gaussian noise that is typically used in the attack. Using a known noise signal allows for less strict tuning of the software-defned radios used in the attack and allows for digital noise removal of the recorded input to enhance the replay attack. Next, we focus on jamming detection from the perspective of the target vehicle. If the vehicle is able to detect that it is being jammed and takes appropriate countermeasures, then the roll-jam attack and other attacks like it would be thwarted. We have created a jamming detection algorithm that is able to use physical layer data to accomplish detection. Our first algorithm focuses on estimating the approximate distance from a potential attacker using received signal power as the primary metric. Our second method involves collecting empirical data and training a machine learning algorithm to perform the jamming detection.

Committee: Eylem Ekici (Committee Member); Can Koksal (Advisor) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2022, Electrical and Computer Engineering

Over the past few decades, the ubiquity of radio-frequency (RF) devices has improved connectivity and productivity in our lives through wireless communication and sensing technologies. To this end, vehicle-to-everything (V2X) communication and vehicular radar imaging technologies have become the key enablers of Intelligent Transportation Systems (ITS) to promote safety, automation, and coordination in traffic. To enable V2X communication, a limited amount of bandwidth in the 5.9 GHz spectrum is dedicated to vehicles for the exchange of basic safety messages with low latency. However, with the large-scale deployment of connected vehicles, the V2X-dedicated band faces the spectrum scarcity problem that lowers the reliability of vehicular communication. The scarcity of dedicated spectrum also limits the feasibility and capabilities of more advanced vehicular applications that rely on broadband communication. Besides, up to 4 GHz of contiguous bandwidth is allocated as the vehicular radar spectrum that is dedicated solely to vehicles in the 76-81 GHz millimeter-wave (mmWave) bands. To supplement V2X communication, the under-utilized vehicular radar spectrum can be leveraged by joint radar-communication (JRC) systems. The objective of JRC is to perform both data transmission and radar imaging using the same \textit{joint} waveform and transceiver hardware. In this dissertation, we investigate transmission optimization and scheduling approaches to enable vehicular JRC in mmWave bands using adaptive orthogonal frequency-division multiplexing (OFDM). First, we study the joint waveform design problem for wideband vehicular JRC. By exploiting the frequency-selectivity in wideband channels, we adaptively design subcarrier coefficients of OFDM to achieve long-range detection and communication performance. We show that the problem is a non-convex quadratically constrained quadratic programming (QCQP), which is NP-hard. As an alternative to existing approaches, we propose time (open full item for complete abstract)

Committee: Eylem Ekici (Advisor); Ness Shroff (Committee Member); Can Emre Koksal (Committee Member) Subjects: Computer Engineering; Electrical Engineering

PhD, University of Cincinnati, 2021, Engineering and Applied Science: Computer Science and Engineering

The demand for video content has skyrocketed in the past decade owing to the popularity of video streaming services such as YouTube and Netflix and the expanding usage of video analytics applications such as surveillance, telemedicine, and public safety. This tremendous demand for video has necessitated providing good quality of experience (QoE) to video application users. Although the concept of QoE is not new, the developments of new network architectures and computational paradigms have led to the need to design QoE-aware video communication frameworks that can handle the new challenges. The overall goal of this dissertation is to study video quality in emerging networks from the perspectives of both human users and video analytic tools and propose new QoE-aware video communication strategies to improve video quality in both cases. Content-Centric Networking (CCN) is a future Internet architecture that has been proposed to tackle the vast amount of global video traffic, provide better scalability, and allow more efficient bandwidth usage. A key feature of CCN is ubiquitous in-network caching, where popular contents are cached near the end-user for faster content fetching. However, this caching mechanism brings new challenges in maintaining QoE for video streaming. We investigated how in-network caching influences video content distribution and video streaming among CCN nodes. Then, we conducted human subjective tests to quantify the influence of the video stalling events on the overall QoE scores. After that, we proposed a new QoE-aware multi-source video streaming algorithm for CCN that aims to suppress the stalling resulted from switching between content sources. The exchange of video among different wireless communication entities has seen an uprise due to the popularity of wireless imaging applications and the Internet of Things (IoT) technology. Software-defined radio (SDR) is a promising technology to communicate across different wireless networ (open full item for complete abstract)

Committee: Rui Dai Ph.D. (Committee Chair); H. Howard Fan Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member); Nan Niu Ph.D. (Committee Member); Carla Purdy Ph.D. (Committee Member) Subjects: Computer Science

Master of Science in Electrical Engineering (MSEE), Wright State University, 2020, Electrical Engineering

Radio Frequency Fingerprinting (RFF) research typically uses expensive, laboratory grade receivers which have high dynamic range, very stable oscillators, large instantaneous bandwidth, multi-rate sampling, etc. In this study, the RFF effectiveness of lower grade receivers is considered. Using software-defined radios (SDRs) of different cost and performance, a linear regression model is developed to predict RFF performance. Unlike two previous studies of SDR effectiveness that used commercial and lab-grade SDRs, the experiment here focused on hobbyist and commercial-grade SDRs (RTL-SDR, B200-mini, N210). A regression model is proposed for a generic SDR. Using a full-factorial experiment matrix, the gain, sample rate, and signal-to-noise ratio (SNR) were selected as the common control factors. The transmit sources were three commercially-available, general purpose, wireless transmitters of the same model. An SDR performance index (SPI) was developed from the percent correct classification using the Random Forest classifier for each SDR and for a generic SDR. The RFF results show that the lower-cost SDRs record the data with enough fidelity to achieve over 90% classification accuracy.

Committee: Michael A. Saville Ph.D., P.E. (Committee Chair); Saiyu Ren Ph.D. (Committee Member); Henry Chen Ph.D. (Committee Member); Joshua N. Ash Ph.D. (Committee Member) Subjects: Electrical Engineering

MS, University of Cincinnati, 2020, Engineering and Applied Science: Electrical Engineering

The following thesis presents a multiuser coordinating scheme used in an indoor positioning system for first responders using the LTE Sidelink waveform on software defined radios (SDR), in order to estimate time-of-arrivals (TOA) between any two devices which are required for locations estimation using the differential Time Difference of Arrival (dTDOA) algorithm. When first responders enter a building to execute a rescue mission, they may not be aware of their surroundings inside the building which may put their lives at risk. The locations estimated from the indoor positioning system can be integrated with Building Information Modeling (BIM) layout, this reduces risk for the first responders as they will be more aware of their surroundings and helps them to carry out their rescue mission safely. The transceiver software for the SDRs was developed by using the LTE Sidelink waveform instead of commercial location services like the global positioning system (GPS) since it cannot penetrate buildings for an indoor setting, or like Wi-Fi signals which may be present inside buildings but may not be available during an emergency situation. The developed transceiver is simulated in MATLAB on one laptop computer going through six different stages of multiuser coordination scheme. For over the air tests in an actual indoor setting, the transceiver software developed on MATLAB is embedded onto software defined radios (SDR) and is tested at the Pavilion building at the University of Cincinnati. The following thesis mainly focuses on the Multi-User Coordination Scheme (MUCS) developed for the indoor positioning system. The over the air testing consists of 6 Universal Software Radio Peripheral (USRPs) SDRs at random locations, each USRP has two channels of which one is used for transmission and other for reception. The performance metrics for evaluating results from simulation and over the air results are average mean error and standard deviation of distance between the es (open full item for complete abstract)

Committee: H. Howard Fan Ph.D. (Committee Chair); Badri Vellambi Ravisankar Ph.D. (Committee Member); Xuefu Zhou Ph.D. (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2020, Electrical and Computer Engineering

This thesis will shown the development of an experimental radar testbed to test cognitive radar applications. The testbed is built upon the Universal Software Radio Peripheral (USRP) X-310, which is a member of a line of software de_ned radio (SDR)s available from Ettus. The testbed can, therefore, be kept at low cost and hence affordable to academia and smaller research projects. Experimental activities presented the system's detection range for a large airliner as approximately 5.5km, for a small aircraft as approximately 2.8km, and a detection range of a small unmanned aerial vehicle (UAV) as more than 350m. An experiment was conducted tracking a small UAV, illustrating that the system is capable of tracking a small target. A method for absolute radar cross section (RCS) calibration and channel characterization is shown. The thesis has also shown the development of a novel cost function for track update interval control using a fully adaptive radar (FAR) framework. An algorithm has been developed for a tracking system with track update interval control using the cost function developed. A simulator written in Matlab tested the algorithm in a set of scenarios. The cognitive radar (CR) experimental testbed was used as a radar system with the adaptive track update interval algorithm implemented. The algorithm was tested through a simple scenario of a UAV ying between two waypoints, and the waypoints were radially to the radar system. Finally, the thesis shows an adaptive beam scheduling method for radar surveillance, where a target present/absent function is used in a FAR framework to increase the cumulative detection performance. Simulation results for multiple maneuvering targets are shown, where the cumulative detection performance for both targets is close to a 100% over the raster beam scheduling method. A many target scenario is shown as well, where the cumulative detection performance is lower than for one or two targets; however, the performance is st (open full item for complete abstract)

Committee: Graeme Smith (Advisor); Joel Johnson (Advisor); Robert Burkholder (Committee Member); Emre Ertin (Committee Member); Daniel Wozniak (Committee Member) Subjects: Electrical Engineering

Master of Science (M.S.), University of Dayton, 2019, Electrical and Computer Engineering

Software Defined Radios (SDRs) have grown in popularity for both military as well as commercial applications. Specifically, the flexibility of using these devices in networked sensor environment is becoming ever more apparent especially in comparison to traditional radios. These networked sensor environments implemented through the use of SDRs are much more flexible in both applications as well as offer a significant cost benefit. SDRs are used to create a (Multiple-Input-Multiple-Output) MIMO antenna array used for beam-forming multiple sparsely distributed antennas. In order to create this beamforming capability, a calibration process is defined that allows multiple antennas the cohere their beams onto a singular location is space. Through the calibration process a networked environment was created to support the calibration process as well as define a client-server relationship between the end user and the system itself. Results indicate that beamforming is possible using SDRs and can produce results similar to a simulated environment.

Committee: Michael Wicks (Committee Chair); Robert Penno (Committee Chair); Guru Subramanyam (Committee Chair) Subjects: Computer Engineering; Electrical Engineering

MS, University of Cincinnati, 2019, Engineering and Applied Science: Electrical Engineering

Multiple antenna technologies are well proven to improve radio-frequency (RF) wireless communications. Many modern communication standards have adopted multiple antenna technologies to provide throughput and reliability gains. Multiple-input, multiple-output (MIMO) refers to using multiple transmit antennas and multiple receive antennas. MIMO is well studied in cellular networks involving base-stations and multiple subscribing users (hub and spoke topologies) and less studied in mobile ad-hoc networks (MANETs). MANETs are infrastructure-less networks where each network node is free to move independently of other nodes. Self-configuring and self-healing algorithms maintain link communications and provide routing capability across the network. In this work, multi-antenna technologies are investigated in order to enhance the throughput and resilience of terrestrial MANETs, such as those used by first responders, in military environments and rural settings. Multi-user (MU-) MIMO offers the potential of a significant leap towards increased data rates, improved spatial reuse, reduced mutual interference, extended reach, and low-probability interception/detection (LPI/LPD). The primary focus in MU-MIMO MANETs is maximizing network sum rates while appropriately handling the multi-user interference generated in mobile networks. Mobile nodes present a constantly changing channel environment which requires a resilient MU-MIMO algorithm to ensure maximized throughput. To provide support for terrestrial concept-of-operations (CONOPS), this work has focused on development of time-division duplex (TDD) MU-MIMO networks. TDD was chosen over frequency-division duplex (FDD) due to the anticipated disruptive, fast-changing channels of terrestrial and low-altitude networks of interest as well as to limit spectral usage. This work relies on using multiple-antenna radio nodes to enable simultaneous link communication between sets of transmitting and receiving nodes. The mutual interf (open full item for complete abstract)

Committee: Xuefu Zhou Ph.D. (Committee Chair); Rui Dai Ph.D. (Committee Member); Robert Hayes PhD (Committee Member); Badri Narayanan Vellambi Ravisankar (Committee Member) Subjects: Electrical Engineering

Master of Science (M.S.), University of Dayton, 2019, Electrical Engineering

Antenna aperture technology is a critical component of any radar system. The aperture of many early radar systems was composed of a a single antenna [1]. As technology advanced, single antenna apertures were replaced by electronically steered arrays for their ability to rapidly steer beams [2]. With the growth in processing capabilities, digitization at every element is becoming increasingly possible. With digitization at every element, array elements no longer must be co located, and instead can be arbitrarily sparse allowing increased flexibility in resulting beam patterns. This capability is of extreme interest in recent years [3] [4] [5] [6]. Key challenges in building arbitrarily sparse arrays is their calibration and measurement. Sparse array calibration becomes challenging in due to the nature of each system being completely independent. Each independent system has its own RF chain including oscillators, amplifiers, exciters and receivers which must be considered during calibration. In some sparse arrays, spatial relationship between antennas is not necessarily fixed. In addition, capturing the radiation patterns of sparse arrays is challenging due to the size of the effective array aperture imposing a need for large chambers to reach the far field. This thesis develops an in-situ calibration technique for arbitrarily sparse arrays, a measurement technique in non-ideal semi-anechoic chamber, and validates the experimental results against industry standard electromagnetic modeling and simulation tools.

Committee: Micheal Wicks Ph.D. (Committee Chair); Guru Subramanyam Ph.D. (Committee Member); Robert Penno Ph.D. (Committee Member); Andrew Kordik M.S. (Committee Member) Subjects: Electrical Engineering

Master of Science, University of Toledo, 2018, Engineering (Computer Science)

Cognitive Radio Network (CRN) technology has offered the opportunistic solution for the spectrum scarcity problem in wireless communication. The Dynamic Spectrum Access (DSA) solution enables radio system to sense and learn the spectrum and reconfigure the parameters to apply cognitive decisions. With these properties, the technology is threatened by attackers and malicious users trying to exploit the network operation and its learning capabilities. Along with traditional threats, a few specific threats have been inadvertently \textit{created} by this technology due to its characteristic behavior and operation. This thesis provides a brief discussion on the threats and attacks with recent contributions on the security of CRN and proposes a security algorithm that uses the Artificial Neural Network (ANN) based machine learning methods to verify incumbent signals in a CRN. The proposed model is trained using Levenberg-Marquardt (LM) algorithm and Scaled Conjugate Gradient (SCG) algorithms to implement signal identification in two sub-categories, namely, known and unknown signals. Signal datasets were collected from the popular NASA Space Communications and Navigation (SCaN) testbed located at international space station (ISS) and also generated from a small in-lab Software Defined Radio (SDR) device to train and test the proposed model. The performances of the two algorithms on multiple datasets were compared using confusion matrices and mean squared error (MSE). Our study concluded that the best performing model exhibits MSE as low as 0.018 and the confusion matrix shows promising results of more than 98\% as the percentage of accurate prediction. The proposed model can be used in a CRN to monitor the signal activity of the users in the network and verify them for genuineness. The model can also alert the system when an unknown user is operating in the network for further security evaluations.

Committee: Ahmad Y. Javaid (Advisor); Weiqing Sun (Committee Co-Chair); Mansoor Alam (Committee Member) Subjects: Computer Science; Electrical Engineering

Master of Science in Electrical Engineering (MSEE), Wright State University, 2017, Electrical Engineering

As communication and radar technology continue to become increasingly sophisticated, the sophistication of technologies used by unintended parties to acquire transmitted information increases in direct proportion. As a result, entities such as the military and commercial communication industries require methods to protect transmitted information from undesired recipients. Furthermore, the frequency spectrum, a finite resource, is becoming increasingly congested due to inefficient utilization. This thesis presents a novel RF steganography concept that uses linear frequency modulated (LFM) radar signals capable of optimizing the use of the frequency spectrum and hiding digital communication within the LFM to covertly transmit to legitimate recipients. Finally, this work demonstrates that these joint radar/communication waveforms can be designed, transmitted, and received, using software defined radio.

Committee: Zhiqiang Wu Ph.D. (Advisor); Xiaodong Zhang Ph.D. (Committee Member); Yan Zhuang Ph.D. (Committee Member) Subjects: Communication; Engineering

Master of Science (M.S.), University of Dayton, 2017, Electrical Engineering

In recent years, ground communications with global positioning system (GPS) satellites has moved from the use of hardware-based receivers to that of the software defined radio (SDR). The use of the SDR has enabled faster and more accurate tracking and communication with the GPS system with minimal increase in hardware requirements. The SDR receiver has become the standard, with little variation in recent years. With the introduction of the concept of narrow correlation [1], a more complete picture of satellite health and signal status can be obtained from the receiver. Using this concept, the ChameleonChips library [2] for Matlab, released in 2012, enables a simulation of a modified narrow correlator to be used in Matlab. The ChameleonChips receiver is then implemented in C++, and it's applications are explored with the intent of moving to a real-time receiver in the future. Both the Matlab and C++ receivers are tested using the same real-world data. The C++ receiver is shown to run acquisition processing 14.2 times faster than the Matlab receiver. Tracking processing in C++ is run 6.01 times faster than Matlab.

Committee: Eric Balster Ph.D. (Advisor); John Macdonald Ph.D. (Committee Member); Frank Scarpino Ph.D. (Committee Member) Subjects: Computer Engineering; Electrical Engineering

Master of Science, Miami University, 2016, Computational Science and Engineering

Automatic modulation classifiers (AMC) are one of the basic building blocks of electronic warfare receivers and cognitive radios. Although many research papers on AMC algorithms have been published, very few results on their implementation are available. This thesis presents a feature-based AMC built upon a software-defined radio platform. The developed AMC can detect signals over a broad spectrum and classify the modulation used. The modulation schemes considered in this thesis are amplitude modulation (AM), frequency modulation (FM), phase-shift keying (PSK), and quadrature amplitude modulation (QAM). Experimental results demonstrate the validity of the developed AMC algorithm and its implementation.

Committee: Chi-Hao Cheng Ph.D (Advisor); Dmitriy Garmatyuk Ph.D (Committee Member); Jason Pennington Ph.D (Committee Member) Subjects: Communication; Computer Engineering; Computer Science; Electrical Engineering; Engineering; Experiments; Technology

Doctor of Philosophy, The Ohio State University, 2013, Electrical and Computer Engineering

Despite the tremendous advancement in innovations in digitizing and processing signals over the last century, real world signals are inevitably analog in nature. Several approaches have been researched and deployed in order to facilitate the maneuver between the digital and analog domains. Specifically, digital-to-analog converters (DACs) are the lynchpin in systems that realize this art of moving from the digital to the analog world. Several real-world imperfections and limitations have impeded the efficacy of DACs in terms of accuracy, speed and power. Howbeit, a myriad of efforts has been made in the recent past towards improving the performance of DACs. Furthermore, the fundamental conception of DACs poses several challenges at the architectural, circuit and technological levels. These challenges mandate a solid understanding in order to enable DAC designers to move up and down the device-circuit-architecture ladder with dexterity. A closer look at the genetic conception of DACs also reveals interesting answers behind some of the well-known questions, such as: How is the spectral performance related to the resolution? Why does distortion in DACs degrade at high frequencies? In order to effectively address these questions, the science behind digital to analog conversion needs to be revisited from a fresh perspective - what is called as a genetic bit-by-bit engineering approach. Such a radical study leads to fascinating ways to conceive DACs that perform well above their expected limit. This dissertation aims to provide a neoteric and holistic view of the fundamental conception, limitations and challenges in how digital data transcends into the analog world. The quantitative study of this process leads to several transformational benefits; of most importance is the ability to engineer D/A converters to provide high resolution performance with low-resolution data, thereby creating the doors to a brand new field of compressive transmission.

Committee: Waleed Khalil (Advisor); John Volakis (Committee Member); Patrick Roblin (Committee Member); Anish Arora (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy (PhD), Wright State University, 2012, Engineering PhD

With the emergence of increasing number of wireless devices and demands for higher data rates, spectrum crowding and congestion increases. Spectrum congestion problem has been challenging wireless communication engineers for a few decades. However, recent studies indicate that most of the time wide ranges of the radio spectrum are rarely utilized. Hence, the spectrum congestion is mainly due to the inefficient spectrum usage rather than the spectrum scarcity. To exploit under used spectrum and utilize the spectrum efficiently in dynamically changing environments, a new technology is needed. Cognitive Radio (CR) arises to be a possible solution to spectral crowding problem by introducing the opportunistic usage of frequency bands that are not heavily occupied by licensed users. In this dissertation, we implement and demonstrate an autonomous cognitive radio system in mobile environment via SDR. We first design and implement an intelligent spectrum sensing engine which can detect the existence of the primary user (PU) signal and accurately estimate its radio frequency (RF) parameters. Second, with the aid of the spectrum sensing engine, a spectrum mask is provided. Meanwhile, a multi-carrier waveform is generated based on spectrally modulated spectrally encoded (SMSE) framework. With the dynamic multi-carrier non-contiguous waveform, an intelligent interference avoidance SMSE-based cognitive radio is implemented and demonstrated using universal software radio peripheral(USRP) and GNU software defined radio (SDR) platform. Third, we propose a novel total intercarrier interference (ICI) cancellation scheme to eliminate the ICI in mobile environment, apply the algorithm to the SMSE base cognitive radio, employ GNU SDR platform and USRP, implement and demonstrate an SMSE based cognitive radio in high mobility environment. Combined with the spectrum sensing engine, the cognitive radio is capable of detecting the availability of each and every subcarrier in the operational (open full item for complete abstract)

Committee: Zhiqiang Wu PhD (Advisor); Henry Chen PhD (Committee Member); Ryan Thomas PhD (Committee Member); Yong Pei PhD (Committee Member); Yan Zhuang PhD (Committee Member) Subjects: Educational Tests and Measurements; Educational Theory

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

Master of Science in Engineering (MSEgr), Wright State University, 2008, Electrical Engineering

A cognitive radio is a radio with built-in intelligence that makes it able to utilize the radio frequency spectrum more efficiently by adapting to the changing conditions and frequency availability. In this thesis a spectrum sensor radio is designed that detects transmissions in FM band and determine the range of used and unused frequencies within the band. The radio then starts a transmission on a frequency among the available frequencies. This intelligent radio system design is implemented using the Universal Software Radio Peripheral and GNU Radio with a program written in python that uses a number of GNU Radio modules along with other python and C-Shell scripts giving a working baseline structure of a cognitive radio.

Committee: Zhiqiang Wu PhD (Advisor); Yong Pei PhD (Committee Member); Xiaodong Zhang PhD (Committee Member) Subjects: Electrical Engineering; Engineering