The objective of this research is to investigate the power-control problem and analyze the network capacity in cognitive radio (CR) networks. For CR users or Secondary users (SUs), two spectrum-access schemes exist: namely, spectrum underlay and spectrum overlay. Spectrum overlay improves the spectrum utilization by granting SUs the authority to sense and explore the unused spectrum bands provided by PUs. in this scheme, designing effective spectrum-sensing techniques in PHY layer is the major concern. Spectrum underlay permits Sus to share the same spectrum bands with PUS at the same time and location. In this scheme, designing robust power control algorithms that guarantee the QoS of both primary and secondary transmissions is the main task. In this thesis, we first investigate the power-control problems in CR networks. Especially, we conduct two research works on power control for CDMA and OFDMA CR networks. Being aware of the competitive spectrum-access feature of SUs, the non-cooperative game theory, as a standard mathematics, is used to study the power-control problem. Note that game-theoretical approaches provide distributed solutions for CR networks,, which fits the needs of CR networks. However, it requires channel state information (CSI) exchange among all SUs, which will cause great overheads in the large network deployment. To gain better network scalability and design more robust power-control algorithm for any hostile radio-access environments, we propose a reinforcement-learning-based repeated power-control game that solve the problem for the first time. The left part of the dissertation is to study the throughput capacity scaling of the newly arising cognitive ad hoc networks (CRAHNs). Stimulated by the seminal work of Gupta and Kumar, the fundamental throughput scaling law for large-scale wireless ad hoc networks has become an active research topic. This research is of great theoretical value for wireless ad hoc networks. Our proposed research studies it in the scenario of CRAHNs under the impact of PU activity. It is a typical and important network scenario that has never been studied yet. We do believe this research has its unique value, it will have an impact to the research community.

The wireless spectrum is currently regulated by government agencies and is assigned to license holders or services on a long-term basis over vast geographical regions. Recent research has shown that a large portion of the assigned spectrum is used sporadically, leading to underutilization and waste of valuable frequency resources. Consequently, dynamic spectrum access techniques are proposed to solve these current spectrum inefficiency problems. This new area of research foresees the development of cognitive radio (CR) networks to further improve spectrum efficiency. The basic idea of CR networks is that the unlicensed devices (also called CR users) share wireless channels with the licensed devices (also known as primary users) that are already using an assigned spectrum. CR networks, however, impose unique challenges resulting from high fluctuation in the available spectrum, as well as diverse quality-of-service (QoS) requirements. These challenges necessitate novel cross-layer techniques that simultaneously address a wide range of communication problems from radio frequency (RF) design to communication protocols, which can be realized through spectrum management functions as follows: (1) determine the portions of the spectrum currently available (spectrum sensing), (2) select the best available channel (spectrum decision), (3) coordinate access to this channel with other users (spectrum sharing), and (4) effectively vacate the channel when a primary user is detected (spectrum mobility). In this thesis, a spectrum management framework for CR networks is investigated that enables seamless integration of CR technology with existing networks. First, an optimal spectrum sensing framework is developed to achieve maximum spectrum opportunities while satisfying interference constraints, which can be extended to multi-spectrum/multi-user CR networks through the proposed sensing scheduling and adaptive cooperation methods. Second, a QoS-aware spectrum decision framework is proposed where spectrum bands are determined by considering the application requirements as well as the dynamic nature of the spectrum bands. Moreover, a dynamic admission control scheme is developed to decide on the spectrum bands adaptively dependent on the time-varying CR network capacity. Next, for spectrum sharing in infrastructure-based CR networks, a joint spectrum and power allocation scheme is proposed to achieve fair resource allocation as well as maximum capacity by opportunistically negotiating additional spectrum based on the licensed user activity (exclusive allocation) and having a share of reserved spectrum for each cell (common use sharing). Finally, we propose a novel CR cellular network architecture based on the spectrum-pooling concept, which mitigates the heterogeneous spectrum availability. Based on this architecture, a unified mobility management framework is devised to support both user and spectrum mobilities in CR networks.

Cognitive radio (CR) technology allows devices to share the wireless spectrum with other users that have a license for operation in these spectrum bands. This area of research promises to solve the problem of spectrum scarcity in the unlicensed bands, and improve the inefficient spectrum utilization in the bands reserved for the licensed users. However, the opportunistic use of the available spectrum by the CR users must not affect the licensed users. This raises several concerns regarding spectrum sensing, sharing and reliable end-to-end communication in CR networks. This thesis is concerned with the design and implementation of communication protocols for the multi-hop infrastructure-less CR ad-hoc networks (CRAHNs). In addition, it also addresses the critical issue of interference-free spectrum usage in specific ad-hoc architectures, such as, resource-constrained wireless sensor networks and wireless mesh networks that have high traffic volumes. The problems of spectrum management that are unique to CR networks are first identified in this thesis. These issues are then addressed at each layer of the network protocol stack while considering the distributed operation in CRAHNs. At the physical layer an algorithmic suite is proposed that allows the CR devices to detect and adapt to the presence of wireless LANs and commercial microwave ovens. A common control channel is designed that allows sharing of the spectrum information between the CR users, even when the available spectrum varies dynamically. A spectrum sharing scheme for mesh networks is proposed at the link layer that allows cooperative detection of the licensed users and fair utilization of the available spectrum among the mesh devices. The spectrum availability and route formation are then considered jointly at the network layer, so that the licensed users are protected as well as the CRAHN performance is maximized. Finally, we extend the classical TCP at the transport layer to ensure end-to-end reliability in a multi-hop CR environment.

Improving the efficiency of spectrum access and utilization under the umbrella of cognitive radio (CR) is one of the most crucial research areas for nearly two decades. The results have been algorithms called cognitive radio engines which use machine learning (ML) to learn and adapt the communication's link based on the operating scenarios. While a number of algorithms for cognitive engine design have been identified, it is widely understood that significant room remains to grow the capabilities of the cognitive engines, and substantially better spectrum utilization and higher throughput can be achieved if cognitive engines are improved. This requires working through some difficult challenges and takes an innovative look at the problem. A tenet of the existing cognitive engine designs is that they are usually designed around one primary ML algorithm or framework. In this dissertation, we discover that it is entirely possible for an algorithm to perform better in one operating scenario (combination of channel conditions, available energy, and operational objectives such as max throughput, and max energy efficiency) while performing less effectively in other operating scenarios. This arises due to the unique behavior of an individual ML algorithm regardless of its operating conditions. Therefore, there is no individual algorithm or parameter sets that have superiority in performance over all other algorithms or parameter sets in all operating scenarios. Using the same algorithm at all times may present a performance that is acceptable, yet may not be the best possible performance under all operating scenarios we are faced with over time. Ideally, the system should be able to adapt its behavior by switching between various ML algorithms or adjusting the operating ML algorithm for the prevailing operating conditions and goal. In this dissertation, we introduce a novel architecture for cognitive radio engines, with the goal of better cognitive engines for improved link adaptation in order to enhance spectrum utilization. This architecture is capable of meta-reasoning and metacognition and the algorithms developed based on this architecture are called metacognitive engines (meta-CE). Meta-reasoning and metacognitive abilities provide for self-assessment, self-awareness, and inherent use and adaptation of multiple methods for link adaptation and utilization. In this work, we provide four different implementation instances of the proposed meta-CE architecture. First, a meta-CE which is equipped with a classification algorithm to find the most appropriate individual cognitive engine algorithm for each operating scenario. The meta-CE switches between the individual cognitive engine algorithms to decrease the training period of the learning algorithms and not only find the most optimal communication configuration in the fastest possible time but also provide the acceptable performance during its training period. Second, we provide different knowledge indicators for estimating the experience level of cognitive engine algorithms. We introduce a meta-CE equipped with these knowledge indicators extracted from metacognitive knowledge component. This meta-CE adjusts the exploration factors of learning algorithms to gain higher performance and decrease training time. The third implementation of meta-CE is based on the robust training algorithm (RoTA) which switches and adjusts the individual cognitive engine algorithms to guarantee a minimum performance level during the training phase. This meta-CE is also equipped with forgetting factor to deal with non-stationary channel scenarios. The last implementation of meta-CE enables the individual cognitive engine algorithms to handle delayed feedback scenarios. We analyze the impact of delayed feedback on cognitive radio engines' performances in two cases of constant and varying delay. Then we propose two meta-CEs to address the delayed feedback problem in cognitive engine algorithms. Our experimental results show that the meta-CE approach, when utilized for a CRS engine performed about 20 percent better (total throughput) than the second best performing algorithm, because of its ability to learn about its own learning and adaptation. In effect, the meta-CE is able to deliver about 70% more data than the CE with the fixed exploration rate in the 1000 decision steps. Moreover, the knowledge indicator (KI) autocorrelation plots show that the proposed KIs can predict the performance of the CEs as early as 100 time steps in advance. In non-stationary environments, the proposed RoTA based meta-CE guarantees the minimum required performance of a CRS while it’s searching for the optimal communication configurations. The RoTA based meta-CE delivers at least about 45% more data than the other algorithms in non-stationary scenarios when the channel conditions are often fluctuating. Furthermore, in delayed feedback scenarios, our results show that the proposed meta-CE algorithms are able to mitigate the adverse impact of delay in low latency scenarios and relieve the effects in high latency situations. The proposed algorithms show a minimum of 15% improvement in their performance compared to the other available delayed feedback strategies in literature. We also empirically tested the algorithms introduced in this dissertation and verified the results therein by designing an over the air (OTA) radio setup. For our experiments, we used GNU Radio and LiquidDSP as free software development toolkits that provide signal processing blocks to implement software-defined radios and signal-processing systems such as modulation, pulse-shaping, frame detection, equalization, and others. We also used two USRP N200 with WBX daughterboards, one as a transmitter and the other as a receiver. In these experiments, we monitored the packet success rate (PSR), throughput, and total data transferred as our key performance indicators (KPI). Then, we tested different proposed meta-CE algorithms in this dissertation to verify the productivity of the proposed algorithms in an OTA real-time radio setup. We showed that the experiments’ outputs support our simulations results as well.

With the advent of ubiquitous computing and Internet of Things (IoT), potentially billions of devices will create a broad range of data services and applications, which will require the communication networks to efficiently manage the increasing complexity. Cognitive network has been envisioned as a new paradigm to address this challenge, which has the capability of reasoning, planning and learning by incorporating cutting edge technologies including knowledge representation, context awareness, network optimization and machine learning. Cognitive network spans over the entire communication system including the core network and wireless links across the entire protocol stack. Cognitive Radio Network (CRN) is a part of cognitive network over wireless links, which endeavors to better utilize the spectrum resources. Core network provides a reliable backend infrastructure to the entire communication system. However, the CR communication and core network infrastructure have attracted various security threats, which become increasingly severe in pace with the growing complexity and adversity of the modern Internet. The focus of this dissertation is to exploit the security vulnerabilities of the state-of-the-art cognitive communication systems, and to provide detection, mitigation and protection mechanisms to allow security enhanced cognitive communications including wireless communications in CRNs and wired communications in core networks. In order to provide secure and reliable communications in CRNs: emph{first}, we incorporate security mechanisms into fundamental CRN functions, such as secure spectrum sensing techniques that will ensure trustworthy reporting of spectrum reading. emph{Second}, as no security mechanism can completely prevent all potential threats from entering CRNs, we design a systematic passive monitoring framework, emph{SpecMonitor}, based on unsupervised machine learning methods to strategically monitor the network traffic and operations in order to detect abnormal and malicious behaviors. emph{Third}, highly capable cognitive radios allow more sophisticated reactive jamming attack, which imposes a serious threat to CR communications. By exploiting MIMO interference cancellation techniques, we propose jamming resilient CR communication mechanisms to survive in the presence of reactive jammers. Finally, we focus on protecting the core network from botnet threats by applying cognitive technologies to detect network-wide Peer-to-Peer (P2P) botnets, which leads to the design of a data-driven botnet detection system, called emph{PeerClean}. In all the four research thrusts, we present thorough security analysis, extensive simulations and testbed evaluations based on real-world implementations. Our results demonstrate that the proposed defense mechanisms can effectively and efficiently counteract sophisticated yet powerful attacks. / Ph. D.

Cognitive radio (CR) networks and heterogeneous cellular networks are promising approaches to satisfy the demand for higher data rates and better connectivity. A CR network increases the utilization of the radio spectrum by opportunistically using it. Heterogeneous networks provide high data rates and improved connectivity by spatially reusing the spectrum and by bringing the network closer to the user. Interference presents a critical challenge for reliable communication in these networks. Accurately modeling it is essential in ensuring a successful design and deployment of these networks. We first propose modeling the aggregate interference power at a primary receiver (PU-Rx) caused from transmissions by randomly located cognitive users (CUs) in a CR network as a shifted lognormal random process. Its parameters are determined using a moment matching method. Extensive benchmarking shows that the proposed model is more accurate than the lognormal and Gaussian process models considered in the literature, even for a relatively dense deployment of CUs. It also compares favorably with the asymptotically exact stable and symmetric truncated stable distribution models, except at high CU densities. Our model accounts for the effect of imperfect spectrum sensing, interweave and underlay modes of CR operation, and path-loss, time-correlated shad-owing and fading of the various links in the network. It leads to new expressions for the probability distribution function, level crossing rate (LCR), and average exceedance duration (AED). The impact of cooperative spectrum sensing is also characterized. We also apply and validate the proposed model by using it to redesign the primary exclusive zone to account for the time-varying nature of interference. Next we model the uplink inter-cell aggregate interference power in homogeneous and heterogeneous cellular systems as a simpler lognormal random variable. We develop a new moment generating function (MGF) matching method to determine the lognormal’s parameters. Our model accounts for the transmit power control, peak transmit power constraint, small scale fading and large scale shadowing, and randomness in the number of interfering mobile stations and their locations. In heterogeneous net-works, the random nature of the number and locations of low power base stations is also accounted for. The accuracy of the proposed model is verified for both small and large values of interference. While not perfect, it is more accurate than the conventional Gaussian and moment-matching-based lognormal and Gamma distribution models. It is also performs better than the symmetric-truncated stable and stable distribution models, except at higher user density.

ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Emerging and next-generation test instrumentation increasingly relies on network communication to manage complex and dynamic test scenarios, particularly for uninhabited autonomous systems. Adapting wireless communication infrastructure to accommodate challenging testing needs can benefit from reconfigurable radio technology. Frequency agility is one characteristic of reconfigurable radios that to date has seen only limited progress toward programmability. This paper overviews an ongoing project to validate a promising chipset that performs conversion of RF signals directly into digital data for the wireless receiver and, for the transmitter, converts digital data into RF signals. The Software Configurable Multichannel Transceiver (SCMT) enables four transmitters and four receivers in a single unit, programmable for any frequency band between 1 MHz and 6 GHz.

ITC/USA 2008 Conference Proceedings / The Forty-Fourth Annual International Telemetering Conference and Technical Exhibition / October 27-30, 2008 / Town and Country Resort & Convention Center, San Diego, California / In a multi-user cognitive radio network, there arises a need for coordination among the network users for efficient utilization of the available electromagnetic spectrum. While adaptive modulation alone helps cognitive radios actively determine the channel quality metric for the next transmission, Game theory combined with an adaptive modulation system helps them achieve mutual coordination among channel users and avoids any possible confusion about transmitting/receiving through a channel in the future. This paper highlights how the concepts of game theory and adaptive modulation can be incorporated in a cognitive radio framework to achieve better communication for telemetry applications.

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Cognitive radio is a reasonably new branch of research aimed at more fully utilizing the RF spectrum. This is accomplished by allowing wireless communication systems to dynamically choose a frequency band, and a modulation technique, based on the current state of the RF spectrum as perceived by the cognitive radio network. This paper will give a brief introduction of cognitive radio networks, and describe a hardware platform designed at the IFT/UMR Telemetry Learning Center. The test-bed will accommodate future research into cognitive networks, by allowing the user to dynamically change both its carrier frequency and modulation technique through software. A general description of the design of the platform is provided.

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / While investigating methods for more efficiently allocating the available spectrum researchers noticed that in many geographical locations, there are broad bands of frequencies that are lightly utilized. Such inefficiencies are inevitable with fixed spectral allocation rules. Cognitive Radios actively measure the spectral utilization and adapt their modulation, frequencies, bandwidths, power, etc. to take advantage of these lightly used “spectral holes” or “white spaces”. Much of the research work in cognitive radios has not taken into account some of the challenges faced in the telemetry community-including multipaths and a guaranteed quality of service. This paper highlights how some mathematical models of adaptive modulation discussed extensively in many research papers and textbooks can be used in Cognitive Radios as well.