As wireless technologies evolve, novel innovations and concepts are required to dynamically and automatically alter various radio parameters in accordance with the radio environment. These innovations open the door for cognitive radio (CR), a new concept in telecommunications. CR makes its decisions using an inference engine, which can learn and adapt to changes in radio conditions. Fuzzy logic (FL) is the proposed decision-making algorithm for controlling the CR's inference engine. Fuzzy logic is well-suited for vague environments in which incomplete and heterogeneous information is present. In our proposed approach, FL is used to alter various radio parameters according to experience gained from different environmental conditions. FL requires a set of decision-making rules, which can vary according to radio conditions, but anomalies rise among these rules, causing degradation in the CR's performance. In such cases, the CR requires a method for eliminating such anomalies. In our model, we used a method based on the Dempster-Shafer (DS) theory of belief to accomplish this task. Through extensive simulation results and vast case studies, the use of the DS theory indeed improved the CR's decision-making capability. Using FL and the DS theory of belief is considered a vital module in the automation of various radio parameters for coping with the dynamic wireless environment. To demonstrate the FL inference engine, we propose a CR version of WiMAX, which we call CogMAX, to control different radio resources. Some of the physical parameters that can be altered for better results and performance are the physical layer parameters such as channel estimation technique, the number of subcarriers used for channel estimation, the modulation technique, and the code rate. / Ph. D.

The tremendous success of various wireless applications operating in unlicensed bands has resulted in the overcrowding of those bands. Cognitive radio (CR) is a new technology that enables an unlicensed user to coexist with incumbent users in licensed spectrum bands without inducing interference to incumbent communications. This technology can significantly alleviate the spectrum shortage problem and improve the efficiency of spectrum utilization. Networks consisting of CR nodes (i.e., CR networks)---often called dynamic spectrum access networks or NeXt Generation (XG) communication networks---are envisioned to provide high bandwidth to mobile users via heterogeneous wireless architectures and dynamic spectrum access techniques. In recent years, the operational aspects of CR networks have attracted great research interest. However, research on the security aspects of CR networks has been very limited. In this thesis, we discuss security issues that pose a serious threat to CR networks. Specifically, we focus on three potential attacks that can be launched at the physical or MAC layer of a CR network: primary user emulation (PUE) attack, spectrum sensing data falsification (SSDF) attack, and control channel jamming (CCJ) attack. These attacks can wreak havoc to the normal operation of CR networks. After identifying and analyzing the attacks, we discuss countermeasures. For PUE attacks, we propose a transmitter verification scheme for attack detection. The scheme utilizes the location information of transmitters together with their signal characteristics to verify licensed users and detect PUE attackers. For both SSDF attacks and CCJ attacks, we seek countermeasures for attack mitigation. In particular, we propose Weighted Sequential Probability Ratio Test (WSPRT) as a data fusion technique that is robust against SSDF attacks, and introduce a multiple-rendezvous cognitive MAC (MRCMAC) protocol that is robust against CCJ attacks. Using security analysis and extensive numerical results, we show that the proposed schemes can effectively counter the aforementioned attacks in CR networks. / Ph. D.

Being one of the fastest growing fields of engineering, wireless has gained the attention of researchers and commercial businesses all over the world. Extensive research is underway to improve the performance of existing systems and to introduce cutting edge wireless technologies that can make high speed wireless communications possible. The first part of this dissertation deals with discrete channel models that are used for simulating error traces produced by wireless channels. Most of the time, wireless channels have memory and we rely on discrete time Markov models to simulate them. The primary advantage of using these models is rapid experimentation and prototyping. Efficient estimation of the parameters of a Markov model (including its number of states) is important to reproducing and/or forecasting channel statistics accurately. Although the parameter estimation of Markov processes has been studied extensively, its order estimation problem has been addressed only recently. In this report, we investigate the existing order estimation techniques for Markov chains and hidden Markov models. Performance comparison with semi-hidden Markov models is also discussed. Error source modeling in slow and fast fading conditions is also considered in great detail. Cognitive Radio is an emerging technology in wireless communications that can improve the utilization of radio spectrum by incorporating some intelligence in its design. It can adapt with the environment and can change its particular transmission or reception parameters to execute its tasks without interfering with the licensed users. One problem that CR network usually faces is the difficulty in detecting and classifying its low power signal that is present in the environment. Most of the time traditional energy detection techniques fail to detect these signals because of their low SNRs. In the second part of this thesis, we address this problem by using higher order statistics of incoming signals and classifying them by using the pattern recognition capabilities of HMMs combined with cased-based learning approach. This dissertation also deals with dynamic spectrum allocation in cognitive radio using HMMs. CR networks that are capable of using frequency bands assigned to licensed users, apart from utilizing unlicensed bands such as UNII radio band or ISM band, are also called Licensed Band Cognitive Radios. In our novel work, the dynamic spectrum management or dynamic frequency allocation is performed by the help of HMM predictions. This work is based on the idea that if Markov models can accurately model spectrum usage patterns of different licensed users, then it should also correctly predict the spectrum holes and use these frequencies for its data transmission. Simulations have shown that HMMs prediction results are quite accurate and can help in avoiding CR interference with the primary licensed users and vice versa. At the same time, this helps in sending its data over these channels more reliably. / Ph. D.

Third Generation (3G) wireless networks have been well studied and optimized with traditional radio resource management techniques, but still there is room for improvement. Cognitive radio technology can bring significantcant network improvements by providing awareness to the surrounding radio environment, exploiting previous network knowledge and optimizing the use of resources using machine learning and artificial intelligence techniques. Cognitive radio can also co-exist with legacy equipment thus acting as a bridge among heterogeneous communication systems. In this work, an approach for applying cognition in wireless networks is presented. Also, two machine learning techniques are used to create a hybrid cognitive engine. Furthermore, the concept of cognitive radio resource management along with some of the network applications are discussed. To evaluate the proposed approach cognition is applied to three typical wireless network problems: improving coverage, handover management and determining recurring policy events. A cognitive engine, that uses case-based reasoning and a decision tree algorithm is developed. The engine learns the coverage of a cell solely from observations, predicts when a handover is necessary and determines policy patterns, solely from environment observations. / Ph. D.

With recent advances in electronics and communication technologies, our daily lives are immersed in an environment of Internet-connected smart things. Despite the great convenience brought by the development of these technologies, privacy concerns and security issues are two topics that deserve more attention. On one hand, as smart things continue to grow in their abilities to sense the physical world and capabilities to send information out through the Internet, they have the potential to be used for surveillance of any individuals secretly. Nevertheless, people tend to adopt wearable devices without fully understanding what private information can be inferred and leaked through sensor data. On the other hand, security issues become even more serious and lethal with the world embracing the Internet of Things (IoT). Failures in computing systems are common, however, a failure now in IoT may harm people's lives. As demonstrated in both academic research and industrial practice, a software vulnerability hidden in a smart vehicle may lead to a remote attack that subverts a driver's control of the vehicle. Our approach to the aforementioned challenges starts by understanding privacy leakage in the IoT era and follows with adding defense layers to the IoT system with attackers gaining increasing capabilities. The first question we ask ourselves is "what new privacy concerns do IoT bring". We focus on discovering information leakage beyond people's common sense from even seemingly benign signals. We explore how much private information we can extract by designing information extraction systems. Through our research, we argue for stricter access control on newly coming sensors. After noticing the importance of data collected by IoT, we trace where sensitive data goes. In the IoT era, edge nodes are used to process sensitive data. However, a capable attacker may compromise edge nodes. Our second research focuses on applying trusted hardware to build trust in large-scale networks under this circumstance. The application of trusted hardware protects sensitive data from compromised edge nodes. Nonetheless, if an attacker becomes more powerful and embeds malicious logic into code for trusted hardware during the development phase, he still can secretly steal private data. In our third research, we design a static analyzer for detecting malicious logic hidden inside code for trusted hardware. Other than the privacy concern of data collected, another important aspect of IoT is that it affects the physical world. Our last piece of research work enables a user to verify the continuous execution state of an unmanned vehicle. This way, people can trust the integrity of the past and present state of the unmanned vehicle. / Doctor of Philosophy / The past few years have witnessed a rising in computing and networking technologies. Such advances enable the new paradigm, IoT, which brings great convenience to people's life. Large technology companies like Google, Apple, Amazon are creating smart devices such as smartwatch, smart home, drones, etc. Compared to the traditional internet, IoT can provide services beyond digital information by interacting with the physical world by its sensors and actuators. While the deployment of IoT brings value in various aspects of our society, the lucrative reward from cyber-crimes also increases in the upcoming IoT era. Two unique privacy and security concerns are emerging for IoT. On one hand, IoT brings a large volume of new sensors that are deployed ubiquitously and collect data 24/7. User's privacy is a big concern in this circumstance because collected sensor data may be used to infer a user's private activities. On the other hand, cyber-attacks now harm not only cyberspace but also the physical world. A failure in IoT devices could result in loss of human life. For example, a remotely hacked vehicle could shut down its engine on the highway regardless of the driver's operation. Our approach to emerging privacy and security concerns consists of two directions. The first direction targets at privacy protection. We first look at the privacy impact of upcoming ubiquitous sensing and argue for stricter access control on smart devices. Then, we follow the data flow of private data and propose solutions to protect private data from the networking and cloud computing infrastructure. The other direction aims at protecting the physical world. We propose an innovative method to verify the cyber state of IoT devices.

Common control channels in cognitive radio (CR) ad hoc networks are spectrum resources temporarily allocated and commonly available to CR users for control message exchange. With no presumably available network infrastructure, CR users rely on cooperation to perform spectrum management functions. One the one hand, CR users need to cooperate to establish common control channels, but on the other hand, they need to have common control channels to facilitate such cooperation. This control channel problem is further complicated by primary user (PU) activities, channel impairments, and intelligent attackers. Therefore, how to reliably and securely establish control links in CR ad hoc networks is a challenging problem. In this work, a framework for control channel design and analysis is proposed to address control channel reliability and security challenges for seamless communication and spectral efficiency in CR ad hoc networks. The framework tackles the problem from three perspectives: (i) responsiveness to PU activities: an efficient recovery control channel method is devised to efficiently establish control links and extend control channel coverage upon PU's return while mitigating the interference with PUs, (ii) robustness to channel impairments: a reinforcement learning-based cooperative sensing method is introduced to improve cooperative gain and mitigate cooperation overhead, and (iii) resilience to jamming attacks: a jamming-resilient control channel method is developed to combat jamming under the impacts of PU activities and spectrum sensing errors by leveraging intrusion defense strategies. This research is particularly attractive to emergency relief, public safety, military, and commercial applications where CR users are highly likely to operate in spectrum-scarce or hostile environment.

Abstract With the current technological advancements, stage is being set for new ultra-responsive and robust 5G-enabled applications (e.g., virtual reality, Tactile Internet,…) to deliver critical real-time traffic. The emergence of such critical applications requires new networking models that can handle more connected devices with super high reliability and low latency communications. In the view of these research challenges, this thesis aims to propose new techno-economic models and networking paradigms needed in the redesign of wireless network architectures and protocols to support the connectivity requirements by which operators and users effectively benefit from new opportunities introduced by 5G-enabled applications. In this thesis, new paradigms in wireless network access are presented and analyzed. First, dynamic network architecture (DNA) is introduced, where certain classes of wireless terminals can be turned temporarily into an access point (AP) anytime while connected to the Internet. In this concept, a framework is proposed to optimize different aspects of this architecture. Furthermore, to dynamically reconfigure an optimum topology and adjust it to the traffic variations, a new specific encoding of genetic algorithm (GA) is presented. Then, a distributed user-centric spectrum sharing is developed based on DNA networks to enable user-provided access points pervasively share the unused resources. Next, a flexible cloud-based radio access network (FRAN) is proposed to offload traffic to DNA networks in order to provide low latency communications. In the sequel of the thesis, as a new paradigm, a context-aware resource allocation scheme based on adaptive spatial beamforming and reinforcement learning is proposed. In addition, semi-cognitive radio network (SCRN) as a new spectrum sharing model is developed to improve the utility of primary and secondary owners. / Tiivistelmä Nykyaikaisilla teknologisilla edistysaskeleilla mahdollistetaan uusien 5G-pohjaisien erittäin lyhyen vasteajan ja suuren luotettavuuden sovelluksien ilmestyminen kriittisen reaaliaikaisen informaation välittämiseen (esim. taktiiliset ja virtuaalitodellisuus-sovellukset). Näiden kaltaiset sovellukset vaativat uudenlaisia verkottumismalleja, jotka kykenevät käsittelemään enemmän laitteita suurella toimintavarmuudella ja matalalla latenssilla. Tämä väitöskirja ehdottaa näiden haasteiden valossa uusia teknis-taloudellisia malleja ja verkottumisparadigmoja, joita tarvitaan verkkoarkkitehtuurien ja -protokollien uudelleensuunnittelussa tulevaisuuden sovelluksien tarpeet huomioiden, joiden kautta operaattorit ja käyttäjät voivat hyödyntää tulevien 5G-sovelluksien tuomat mahdollisuudet. Tässä väitöskirjassa esitetään ja analysoidaan uusia paradigmoja langattomaan verkkoliityntään. Ensimmäisenä esitellään dynaaminen verkkoarkkitehtuuri (dynamic network architecture, DNA), missä tietyt langattomat terminaalit voidaan väliaikaisesti muuttaa liityntäpisteiksi milloin vain internetyhteyden ollessa käytettävissä. Tämän konseptin puitteissa ehdotetaan viitekehys sen eri osa-alueiden optimoimiseksi. Tämän lisäksi esitetään uusi spesifinen geneettisen algoritmin (GA) koodaus optimaalisen topologian dynaamiseen konfigurointiin ja sen säätämiseen tietoliikenteen määrän mukaan. Tämän jälkeen esitellään kehitetty hajautettu käyttäjäkeskeinen spektrinjako, joka perustuu DNA-verkkoihin ja joka mahdollistaa käyttämättömien resurssien kokonaisvaltaisen jakamisen käyttäjien kautta. Seuraavaksi työssä ehdotetaan joustavaa pilvipalvelu-pohjaista liityntäverkkoa (flexible cloud-based radio access network, FRAN) käyttäjädatan purkamiseksi DNA-verkoille matalalatenssisen tietoliikenteen tarjoamiseksi. Edellä mainittujen menetelmien seurauksena ehdotetaan uutta paradigmaa: Kontekstiriippuvaista resurssien allokointia perustuen adaptiiviseen spatiaaliseen keilanmuodostukseen ja vahvistusoppimiseen. Näiden lisäksi kehitetään uusi spektrinjakomalli puolikognitiivisille radioverkoille (semi-cognitive radio network, SCRN) ensisijaisien ja toissijaisien käyttäjien utiliteetin parantamiseksi.

This thesis proposes a multifaceted production test and post-manufacture yield enhancement framework for RF systems. This framework uses low-cost test and post-manufacture calibration/tuning techniques. Since the test cost and the yield of the RF circuits/sub-system directly contribute to the manufacturing cost of RF systems, the proposed framework minimizes overall RF systems' manufacturing cost by taking two approaches. In the first approach, low-cost testing methodologies are proposed for RF amplifiers and integrated RF substrates with an embedded RF passive filter and interconnect. Techniques are developed to test RF circuits by the analysis of low-frequency signal of the order of few MHz and without using any external RF test-stimulus. Oscillation principles are used to enable testing of RF circuits without any external test-stimulus. In the second approach, to increase the yield of the RF circuits for parametric defects, RF circuits are tuned to compensate for a performance loss during production test using on-board or on-chip resources. This approach includes a diagnosis algorithm to identify faulty circuits within the system, and performs a compensation process that adjusts tunable components to enhance the performance of the RF circuits. In the proposed yield improvement methodologies, the external test stimulus is not required because the stimulus is generated by the RF circuit itself with the help of additional circuitry and faulty circuits are detected using low-cost test methods developed in this research. As a result, the proposed research enables low-cost testing and self-healing of RF systems.

The cognitive radio-based wireless networks have been proposed as a promising technology to improve the utilization of the radio spectrum through opportunistic spectrum access. In this context, the cognitive radios opportunistically access the spectrum which is licensed to primary users when the primary user transmission is detected to be absent. For opportunistic spectrum access, the cognitive radios should sense the radio environment and allocate the spectrum and power based on the sensing results. To this end, in this thesis, I develop a novel cooperative spectrum sensing scheme for cognitive radio networks (CRNs) based on machine learning techniques which are used for pattern classification. In this regard, unsupervised and supervised learning-based classification techniques are implemented for cooperative spectrum sensing. Secondly, I propose a novel joint channel and power allocation scheme for downlink transmission in cellular CRNs. I formulate the downlink resource allocation problem as a generalized spectral-footprint minimization problem. The channel assignment problem for secondary users is solved by applying a modified Hungarian algorithm while the power allocation subproblem is solved by using Lagrangian technique. Specifically, I propose a low-complexity modified Hungarian algorithm for subchannel allocation which exploits the local information in the cost matrix. Finally, I propose a novel dynamic common control channel-based medium access control (MAC) protocol for CRNs. Specifically, unlike the traditional dedicated control channel-based MAC protocols, the proposed MAC protocol eliminates the requirement of a dedicated channel for control information exchange. / October 2015

Cognitive Radio (CR) has received tremendous research attention over the past decade, both in the academia and industry, as it is envisioned as a promising solution to the problem of spectrum scarcity. ACR is a device that senses the spectrum for occupancy by licensed users(also called as primary users), and transmits its data only when the spectrum is sensed to be available. For the efficient utilization of the spectrum while also guaranteeing adequate protection to the licensed user from harmful interference, the CR should be able to sense the spectrum for primary occupancy quickly as well as accurately. This makes Spectrum Sensing(SS) one of the where the goal is to test whether the primary user is inactive(the null or noise-only hypothesis), or not (the alternate or signal-present hypothesis). Computational simplicity, robustness to uncertainties in the knowledge of various noise, signal, and fading parameters, and ability to handle interference or other source of non-Gaussian noise are some of the desirable features of a SS unit in a CR. In many practical applications, CR devices can exploit known structure in the primary signal. IntheIEEE802.22CR standard, the primary signal is a wideband signal, but with a strong narrowband pilot component. In other applications, such as military communications, and blue tooth, the primary signal uses a Frequency Hopping (FH)transmission. These applications can significantly benefit from detection schemes that are tailored for detecting the corresponding primary signals. This thesis develops novel detection schemes and rigorous performance analysis of these primary signals in the presence of fading. For example, in the case of wideband primary signals with a strong narrowband pilot, this thesis answers the further question of whether to use the entire wideband for signal detection, or whether to filter out the pilot signal and use narrowband signal detection. The question is interesting because the fading characteristics of wideband and narrowband signals are fundamentally different. Due to this, it is not obvious which detection scheme will perform better in practical fading environments. At another end of the gamut of SS algorithms, when the CR has no knowledge of the structure or statistics of the primary signal, and when the noise variance is known, Energy Detection (ED) is known to be optimal for SS. However, the performance of the ED is not robust to uncertainties in the noise statistics or under different possible primary signal models. In this case, a natural way to pose the SS problem is as a Goodness-of-Fit Test (GoFT), where the idea is to either accept or reject the noise-only hypothesis. This thesis designs and studies the performance of GoFTs when the noise statistics can even be non-Gaussian, and with heavy tails. Also, the techniques are extended to the cooperative SS scenario where multiple CR nodes record observations using multiple antennas and perform decentralized detection. In this thesis, we study all the issues listed above by considering both single and multiple CR nodes, and evaluating their performance in terms of(a)probability of detection error,(b) sensing-throughput trade off, and(c)probability of rejecting the null-hypothesis. We propose various SS strategies, compare their performance against existing techniques, and discuss their relative advantages and performance tradeoffs. The main contributions of this thesis are as follows: The question of whether to use pilot-based narrowband sensing or wideband sensing is answered using a novel, analytically tractable metric proposed in this thesis called the error exponent with a confidence level. Under a Bayesian framework, obtaining closed form expressions for the optimal detection threshold is difficult. Near-optimal detection thresholds are obtained for most of the commonly encountered fading models. Foran FH primary, using the Fast Fourier Transform (FFT) Averaging Ratio(FAR) algorithm, the sensing-through put trade off are derived in closed form. A GoFT technique based on the statistics of the number of zero-crossings in the observations is proposed, which is robust to uncertainties in the noise statistics, and outperforms existing GoFT-based SS techniques. A multi-dimensional GoFT based on stochastic distances is studied, which pro¬vides better performance compared to some of the existing techniques. A special case, i.e., a test based on the Kullback-Leibler distance is shown to be robust to some uncertainties in the noise process. All of the theoretical results are validated using Monte Carlo simulations. In the case of FH-SS, an implementation of SS using the FAR algorithm on a commercially off-the ¬shelf platform is presented, and the performance recorded using the hardware is shown to corroborate well with the theoretical and simulation-based results. The results in this thesis thus provide a bouquet of SS algorithms that could be useful under different CRSS scenarios.