Cognitive radio (CR) is a multi-disciplinary field that makes use of knowledge from a multitude of specialties such as antenna design, circuits, systems and digital signal processing among many others. CR has emerged as an area of interest over 20 years ago and in the years since has evolved to encompass both realizable theory and physical hardware. Key among the latter are reconfigurable, software defined radios and embedded sensors that incorporate flexible parameters, allowing a CR to operate in a wide variety of electromagnetic (EM) environments. The ideal cognitive radio would be capable of adapting to a changing EM environment without any specific knowledge or direction from the operator. This would require the radio itself to be aware of the EM environment and ideally, to sense the EM environment and act upon it in a semi-autonomous or autonomous way. While most research in this field has focused on the spectrum sensing aspects of the domain, development of the above-described "ideal CR" would require that the EM environment be characterized in domains such as angular, time and polarization among others. Signal dependent parameters can also be characterized such as bandwidth and modulation. The multi-dimensionality of the environment and the signals present within entail challenges with scalability and efficiency. This work focuses on the efficient sensing of signals in the angular domain also known as direction-of-arrival (DOA). There are a multitude of ways to find a signal's DOA. All require multiple antennas connected to a single or multiple radio nodes, antennas with patterns that gather energy in a particular direction, or multiple single antenna radios. The methods that utilize multiple antennas exploit the phase and/or amplitude relationships between the antennas themselves for a signal's DOA. The principal tradeoff between DOA methods typically converges to scan time vs. number of antenna elements. For many DOA architectures, this also means a scan time tradeoff with angular resolution as well. Since fast and accurate measurements are important for characterizing a quickly changing EM environment, sensing speed becomes a key requirement in designing a CR and associated sensing architecture. In this work, we present a DOA sensing architecture suitable for use in CR systems called the Direct Space to Information Converter (DSIC). Unlike current state-of-the art DOA methods, the DSIC breaks the tradeoff between scan time and the number of antenna elements needed for a given angular resolution when compared to other DOA and beamforming architectures. By randomly modulating the received signals in space, across multiple antenna elements and taking a few, compressed sensing (CS) measurements, the DSIC is able to angularly scan a wide field of view in an order of magnitude less time than other DOA methods. These CS measurements correspond to different random perturbations of the DSIC's antenna factor and can be quantized in as little as a single bit of resolution in the DSIC's phaseshifters/vector modulators. The DSIC is able to create multiple user-specified nulls in the antenna pattern to reduce the impact of strong known interferers while also simultaneously scanning the full field of view. Additionally, the designer has the option of performing simultaneous reception or nulling while sensing. If nulling, a few different methods are available each suitable for varying EM environments and potential use cases. We show in detail the multi-disciplinary process in designing a complete end-to-end hardware solution, selecting the parameters necessary to design the DSIC as well as test and characterize it. The benefits of the DSIC are discussed and compared to the current state-of the art with an emphasis on architectures suitable for use in interferer rich environments. We demonstrate that the energy usage of the DSIC is lower than comparable CR architectures by a large factor and scales much more favorably in terms of energy and physical complexity as the number of antenna elements increase. At the conclusion of this work we also discuss future areas of exploration in extending the DSIC's capability by incorporating an ability to sense the spectrum as well as the DOA of a signal.

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Cognitive radio networks (CRNs) are a promising solution to the channel (spectrum) congestion problem. This dissertation presents work on the two main issues in CRNs: spectrum management and cross-layer protocol design. The objective of spectrum management is to enable the efficient usage of spectrum resources in CRNs, which protects primary users' activities and ensures the effective spectrum sharing among nodes. We consider to improve the spectrum sensing efficiency and accuracy, so that the spectrum sensing cost is reduced. We consider the pre-phase of spectrum sensing and provide structures for sensing assistance. Besides the spectrum sensing phase, the sharing of spectrum, or the channel allocation, among nodes is also the main component in the spectrum management. We provide our approach to achieve a reliable and effective channel assignment. The channel availabilities for different nodes in CRNs are dynamic and inconsistent. This poses challenges on the MAC layer protocols for CRNs. Moreover, due to the lack of knowledge on primary users, they can suddenly become available during the secondary users' data transmission. Therefore, for a end-to-end data transmission in CRNs, the routing algorithm is different from the existing routing algorithms in traditional networks. We consider the cross-layer protocol design, and propose the solutions for efficient data transmission. We propose the novel routing protocol design considering the boundaries of PUs. Also, an effective structure for reliable end-to-end data transmission is presented, which makes use of the area routing protocol. We build a USRP/Gnuradio testbed for the performance evaluation of our protocols. / Computer and Information Science

No / Spectrum sensing is used in cognitive radio systems to detect the availability of spectrum holes for secondary usage. The simplest and most famous spectrum sensing techniques are based either on energy detection or eigenspace analysis from Random Matrix Theory (RMT) such as using the Marchenko-Pastur law. These schemes suffer from uncertainty in estimating the noise variance which reduces their performance. In this paper we propose a new method to evaluate the noise variance that can eliminate the limitations of the aforementioned schemes. This method estimates the noise variance from a measurement set of noisy signals or noise-only signals. Extensive simulations show that the proposed method performs well in estimating the noise variance. Its performance greatly improves with increasing numbers of measurements and also with increasing numbers of samples taken per measurement.

No / Dynamic Spectrum Access (DSA) for secondary usage of underutilized radio spectrum is currently of great interest for radio regulatory authorities and for cellular network operators. However, the co-existence of multiple devices operating in the same bands, such as wireless microphones which also operate in TV bands, poses a challenge to DSA. Efficient and proactive spectrum sensing could prevent harmful interference between collocated devices, but existing blind spectrum sensing schemes such as energy detection and schemes based on Random Matrix Theory (RMT) have performance limitations. We propose a new blind spectrum sensing scheme for cognitive radio. The proposed scheme uses a new formula for the estimation of noise variance. The scheme has been evaluated through extensive simulations on wireless microphone signals and shows higher performance as compared to energy detection and RMT-based sensing schemes such as MME and EME. It also shows higher performance in terms of probability of detection (Pd).

For complex computer networks with many tunable parameters and network performance objectives, the task of selecting the ideal network operating state is difficult. To improve the performance of these kinds of networks, this research proposes the idea of the cognitive network. A cognitive network is a network composed of elements that, through learning and reasoning, dynamically adapt to varying network conditions in order to optimize end-to-end performance. In a cognitive network, decisions are made to meet the requirements of the network as a whole, rather than the individual network components. We examine the cognitive network concept by first providing a definition and then outlining the difference between it and other cognitive and cross-layer technologies. From this definition, we develop a general, three-layer cognitive network framework, based loosely on the framework used for cognitive radio. In this framework, we consider the possibility of a cognitive process consisting of one or more cognitive elements, software agents that operate somewhere between autonomy and cooperation. To understand how to design a cognitive network within this framework we identify three critical design decisions that affect the performance of the cognitive network: the selfishness of the cognitive elements, their degree of ignorance, and the amount of control they have over the network. To evaluate the impact of these decisions, we created a metric called the price of a feature, defined as the ratio of the network performance with a certain design decision to the performance without the feature. To further aid in the design of cognitive networks, we identify classes of cognitive networks that are structurally similar to one another. We examined two of these classes: the potential class and the quasi-concave class. Both classes of networks will converge to Nash Equilibrium under selfish behavior and in the quasi-concave class this equilibrium is both Pareto and globally optimal. Furthermore, we found the quasi-concave class has other desirable properties, reacting well to the absence of certain kinds of information and degrading gracefully under reduced network control. In addition to these analytical, high level contributions, we develop cognitive networks for two open problems in resource management for self-organizing networks, validating and illustrating the cognitive network approach. For the first problem, a cognitive network is shown to increase the lifetime of a wireless multicast route by up to 125\%. For this problem, we show that the price of selfishness and control are more significant than the price of ignorance. For the second problem, a cognitive network minimizes the transmission power and spectral impact of a wireless network topology under static and dynamic conditions. The cognitive network, utilizing a distributed, selfish approach, minimizes the maximum power in the topology and reduces (on average) the channel usage to within 12\% of the minimum channel assignment. For this problem, we investigate the price of ignorance under dynamic networks and the cost of maintaining knowledge in the network. Today's computer networking technology will not be able to solve the complex problems that arise from increasingly bandwidth-intensive applications competing for scarce resources. Cognitive networks have the potential to change this trend by adding intelligence to the network. This work introduces the concept and provides a foundation for future investigation and implementation. / Ph. D.

In recent years, cognitive radios and cognitive wireless networks have been introduced as a new paradigm for enabling much higher spectrum utilization, providing more reliable and personal radio services, reducing harmful interference, and facilitating the interoperability or convergence of different wireless communication networks. Cognitive radios are goal-oriented, autonomously learn from experience and adapt to changing operating conditions. Cognitive radios have the potential to drive the next generation of radio devices and wireless communication system design and to enable a variety of niche applications in demanding environments, such as spectrum-sharing networks, public safety, natural disasters, civil emergencies, and military operations. This research first introduces an innovative approach to developing cognitive radios based on the Radio Environment Map (REM). The REM can be viewed as an integrated database that provides multi-domain environmental information and prior knowledge for cognitive radios, such as the geographical features, available services and networks, spectral regulations, locations and activities of neighboring radios, policies of the users and/or service providers, and past experience. The REM, serving as a vehicle of network support to cognitive radios, can be exploited by the cognitive engine for most cognitive functionalities, such as situation awareness, reasoning, learning, planning, and decision support. This research examines the role of the REM in cognitive radio development from a network point of view, and focuses on addressing three specific issues about the REM: how to design and populate the REM; how to exploit the REM with the cognitive engine algorithms; and how to evaluate the performance of the cognitive radios. Applications of the REM to wireless local area networks (WLAN) and wireless regional area networks (WRAN) are investigated, especially from the perspectives of interference management and radio resource management, which illustrate the significance of cognitive radios to the evolution of wireless communications and the revolution in spectral regulation. Network architecture for REM-enabled cognitive radios and framework for REM-enabled situation-aware cognitive engine learning algorithms have been proposed and formalized. As an example, the REM, including the data model and basic application programmer interfaces (API) to the cognitive engine, has been developed for cognitive WRAN systems. Furthermore, REM-enabled cognitive cooperative learning (REM-CCL) and REM-enabled case- and knowledge-based learning algorithms (REM-CKL) have been proposed and validated with link-level or network-level simulations and a WRAN base station cognitive engine testbed. Simulation results demonstrate that the WRAN CE can adapt orders of magnitude faster when using the REM-CKL than when using the genetic algorithms and achieve near-optimal global utility by leveraging the REM-CKL and a local search. Simulation results also suggest that exploiting the Global REM information can considerably improve the performance of both primary and secondary users and mitigate the hidden node (or hidden receiver) problem. REM dissemination schemes and the resulting overhead have been investigated and analyzed under various network scenarios. By extending the optimized link state routing protocol, the overhead of REM dissemination in wireless ad hoc networks via multipoint relays can be significantly reduced by orders of magnitude as compared to plain flooding. Performance metrics for various cognitive radio applications are also proposed. REM-based scenario-driven testing (REM-SDT) has been proposed and employed to evaluate the performances of the cognitive engine and cognitive wireless networks. This research shows that REM is a viable, cost-efficient approach to developing cognitive radios and cognitive wireless networks with significant potential in various applications. Future research recommendations are provided in the conclusion. / Ph. D.

As traditional wireless systems that only operate on fixed frequency bands are reaching their capacity limits, advanced frequency-agile radio systems are developed for more efficient spectrum utilization. For example, white space radios dynamically leverage locally unused TV channels to provide high-speed long-distance connectivity. They have already been deployed to connect the unconnected in rural areas and developing countries. However, such application scenarios are still limited due to low commercial demand. Hence, exploring better applications for white space radios needs more effort. With the benefits come the threats. As frequency-agile radio systems (e.g., software-defined radios) are flexible and become extremely low-cost and small-sized, it is very convenient for attackers to build attacking tools and launch wireless attacks using these radios. For example, civilian GPS signals can be easily spoofed by low-cost portable spoofers built with frequency-agile radio systems. In this dissertation, we study both the threat and application of frequency-agile radio systems. Specifically, our work focuses on the spoofing threat of frequency-agile radio towards GPS-based systems and the application of TV white space radio for ocean communications. Firstly, we explore the feasibility of using frequency-agile radio to stealthily manipulate GPS-based road navigation systems without alerting human drivers. A novel attacking algorithm is proposed, where the frequency-agile radio transmits fake GPS signals to lead the victim to drive on a wrong path that looks very similar with the navigation route on the screen. The attack's feasibility is demonstrated with real-world taxi traces in Manhattan and Boston. We implement a low-cost portable GPS spoofer using an off-the-shelf frequency-agile radio platform to perform physical measurements and real-world driving tests, which shows the low level of difficulty of launching the attack in real road environment. In order to study human-in-the-loop factor, a deceptive user study is conducted and the results show that 95% of the users do not recognize the stealthy attack. Possible countermeasures are summarized and sensor fusion defense is explored with preliminary tests. Secondly, we study similar GPS spoofing attack in database-driven cognitive radio networks. In such a network, a secondary user queries the database for available spectrum based on its GPS location. By manipulating GPS locations of surrounding secondary users with a frequency-agile radio, an attacker can potentially cause serious primary user interference and denial-of-service to secondary users. The serious impact of such attacks is examined in simulations based on the WhiteSpaceFinder spectrum database. Inspired by the characteristics of the centralized system and the receiving capability of cognitive radios, a combination of three defense mechanisms are proposed to mitigate the location spoofing threat. Thirdly, we explore the feasibility of building TV white space radio based on frequency-agile radio platform to provide connectivity on the ocean. We design and implement a low-cost low-power white space router ($523, 12 watts) customized for maritime applications. Its communication capability is confirmed by field link measurements and ocean-surface wave propagation simulations. We propose to combine this radio with an energy harvesting buoy so that the radio can operate independently on the ocean and form a wireless mesh network with other similar radios. / PHD / As traditional wireless systems, such as mobile phones and WiFi access points, only operate on some fixed frequency bands, it becomes increasingly crowded for those popular bands. Hence, for more efficient frequency resource utilization, frequency-agile radio systems that can dynamically operate on different frequency bands are developed. With these new technologies come new threats and applications, which are the focus of our work. On the one hand, as frequency-agile radio systems become low-cost and portable, attackers can easily launch wireless attacks with them. For example, we explored the feasibility, impact, and countermeasures for GPS spoofing attacks using frequency-agile radio systems in different scenarios. In a GPS spoofing attack, an attacker transmits false GPS signals to manipulate users’ GPS receivers. This kind of attack can be very dangerous and even life-threatening if it is launched against critical GPS-based applications. For example, once GPS-based navigation systems in self-driving cars are stealthily manipulated by remote attackers, attackers can divert self-driving cars to pre-defined destinations or dangerous situations like wrong-way driving on highway. On the other hand, since there is rich under-utilized spectrum resource in remote areas with no broadband connection yet, frequency-agile radio systems can be used to provide broadband internet connectivity there. For example, based on frequency-agile radio platform, we developed a low-cost low-power wireless router that can dynamically operate on TV broadcasting band. It is able to provide high-speed wireless connection to a large area on the ocean. This technology has the potential to bring low-cost high-speed connection to people and industry on the ocean, which will facilitate various maritime applications.

This dissertation provides theory and analysis on the impact resource management has on software-defined radio platforms by investigating the inherent trade-off between spectrum and processing effciencies with their relation to both the power consumed by the host processor and the complexity of the algorithm which it can support. The analysis demonstrates that considerable resource savings can be gained without compromising the resulting quality of service to the user, concentrating specifically on physical-layer signal processing elements commonly found in software definitions of single- and multi-carrier communications signals. Novel synchronization techniques and estimators for unknown physical layer reference parameters are introduced which complement the energy-quality scalability of software-defined receivers. A new framing structure is proposed for single-carrier systems which enables fast synchronization of short packet bursts, applicable for use in dynamic spectrum access. The frame is embedded with information describing its own structure, permitting the receiver to automatically modify its software configuration, promoting full waveformfl‚exibility for adapting to quickly changing wireless channels. The synchronizer's acquisition time is reduced by exploiting cyclostationary properties in the preamble of transmitted framing structure, and the results are validated over the air in a wireless multi-path laboratory environment. Multi-carrier analysis is concentrated on synchronizing orthogonal frequency-division multiplexing (OFDM) using offset quadrature amplitude modulation (OFDM/OQAM) which is shown to have significant spectral compactness advantages over traditional OFDM. Demodulation of OFDM/OQAM is accomplished using computationally effcient polyphase analysis filterbanks, enabled by a novel approximate square-root Nyquist filter design based on the near-optimum Kaiser-Bessel window. Furthermore, recovery of sample timing and carrier frequency offsets are shown to be possible entirely in the frequency domain, enabling demodulation in the presence of strong interference signals while promoting heterogeneous signal coexistence in dynamic spectrum environments. Resource management is accomplished through the introduction of a self-monitoring framework which permits system-level feedback to the radio at run time. The architecture permits the radio to monitor its own processor usage, demonstrating considerable savings in computation bandwidths on the tested platform. Resource management is assisted by supervised intelligent heuristic-based learning algorithms which use software-level feedback of the radio's active resource consumption to optimize energy and processing effciencies in dynamic spectrum environments. In particular, a case database-enabled cognitive engine is proposed which abstracts from the radio application by using specific knowledge of previous experience rather than relying on general knowledge within a specific problem domain. / Ph. D.

Jamming is an ongoing threat that plagues wireless communications in contested areas. Unfortunately, jamming complexity and sophistication will continue to increase over time. The traditional approach to addressing the jamming threat is to harden radios, such that they sacrifice communications performance for more advanced jamming protection. To provide an escape from this trend, we investigate the previously unexplored area of jammer exploitation. This dissertation develops the concept of antifragile communications, defined as the capability for a communications system to improve in performance due to a system stressor or harsh condition. Antifragility refers to systems that increase in capability, resilience, or robustness as a result of disorder (e.g., chaos, uncertainty, stress). An antifragile system is fundamentally different from one that is resilient (i.e., able to recover from failure) and robust (i.e., able to resist failure). We apply the concept of antifragility to wireless communications through several novel strategies that all involve exploiting a communications jammer. These strategies can provide an increase in throughput, efficiency, connectivity, or covertness, as a result of the jamming attack itself. Through analysis and simulation, we show that an antifragile gain is possible under a wide array of electronic warfare scenarios. Throughout this dissertation we provide guidelines for realizing these antifragile waveforms. Other major contributions of this dissertation include the development of a communications jamming taxonomy, feasibility study of reactive jamming in a SATCOM-type scenario, and a reinforcement learning-based reactive jamming mitigation strategy, for times when an antifragile approach is not practical. Most of the jammer exploitation strategies described in this dissertation fall under the category of jammer piggybacking, meaning the communications system turns the jammer into an unwitting relay. We study this jammer piggybacking approach under a variety of reactive jamming behaviors, with emphasis on the sense-and-transmit type. One piggybacking approach involves transmitting using a specialized FSK waveform, tailored to exploit a jammer that channelizes a block of spectrum and selectively jams active subchannels. To aid in analysis, we introduce a generalized model for reactive jamming, applicable to both repeater-based and sensing-based jamming behaviors. Despite being limited to electronic warfare scenarios, we hope that this work can pave the way for further research into antifragile communications. / Ph. D.