Cognitive Radio (CR) capitalizes advances in signal processing and radio technology and is capable of reconfiguring RF and switching to desired frequency bands. It is a frequency-agile data communication device that is vastly more powerful than existing multi-channel multi-radio (MC-MR) technology. In this thesis, we investigate the important problem of multi-hop networking with CR nodes. In a CR network, each node has a set of frequency bands (not necessarily of equal size) that may not be the same as those at other nodes. The uneven size of frequency bands prompts the need of further division into sub-bands for optimal spectrum sharing. We characterize behaviors and constraints for such multi-hop CR network from multiple layers, including modeling of spectrum sharing and sub-band division, scheduling and interference constraints, and flow routing. We give a formal mathematical formulation with the objective of maximizing the network throughput for a set of user communication sessions. Since such problem formulation falls into mixed integer non-linear programming (MINLP), which is NP-hard in general, we develop a lower bound for the objective by relaxing the integer variables and linearization. Subsequently, we develop a nearoptimal algorithm to this MINLP problem. This algorithm is based on a novel sequential fixing (SF) procedure, where the integer variables are determined iteratively via a sequence of linear program (LP). In order to implement and evaluate these algorithms in a controlled laboratory setting, we design and implement an emulation testbed. The highlights of our experimental research include: • Emulation of a multi-hop CR network with arbitrary topology; • An implementation of the proposed SF algorithm at the application layer; • A source routing implementation that can easily support comparative study between SF algorithm and other schemes; • Experiments comparing the SF algorithm with another algorithm called Layered Greedy Algorithm (LGA); • Experimental results show that the proposed SF significantly outperforms LGA. In summary, the experimental research in this thesis has demonstrated that SF algorithm is a viable algorithm for optimal spectrum sharing in multi-hop CR networks. / Master of Science

Development of Cognitive Radio (CR) applications, which rely on a radio's ability to adapt intelligently to it's spectral surroundings will soon make the all important technological jump from research interest to systems integration, as demand for highly adaptive wireless applications expand. VT-CORNET (Virginia Tech – Cognitive Radio Network Testbed) is a unique testbed concept, designed to facilitate this technology leap by offering researchers — both local and remote — the opportunity to conduct CR experiments on an installed infrastructure of highly flexible radio nodes. These nodes — 48 in total — are distributed throughout four floors of a building on the Virginia Tech campus, and provide researchers with diverse options in terms of channel conditions and deployment scenarios. The radios themselves consist of the widely used USRP2 Software Defined Radio (SDR) platform, coupled to a centrally located cluster of rack servers — which provide a high performance GPP environment for real-time software based signal processing. VT-CORNET is specially licensed to operate our low-power nodes over a broad range of frequencies, which provides researcher the opportunity to conduct experiments on live spectrum — in the presence of real primary users. Testbeds are a widely used tool in the wireless and networking fields, and VT-CORNET expands the concept through a focus on CR research and education. This thesis describes the construction and deployment of the CORNET testbed in detail. Specific contributions made to the testbed include the design and implementation of the management network, as well as the initial deployment of the SDR nodes in the ceiling. In addition, this thesis describes the administration and management of the CORNET GPP cluster, and provides a instructions for the basic usage of CORNET from an administrative and user perspective. Finally, this thesis describes a number of custom SDR waveforms implemented on CORNET which demonstrate the utility of the testbed for cognitive radio applications. / Master of Science

No / Eigenvalue based spectrum sensing schemes such as Maximum Minimum Eigenvalue (MME), Maximum Energy Detection (MED) and Energy with Minimum Eigenvalue (EME) have higher spectrum sensing performance without requiring any prior knowledge of Primary User (PU) signal but the decision hypothesis used in these eigenvalue based sensing schemes depends on the calculation of maximum eigenvalue from covariance matrix of measured signal. Calculation of the covariance matrix followed by eigenspace analysis of the covariance matrix is a resource intensive operation and takes overhead time during critical process of spectrum sensing. In this paper we propose a new blind spectrum sensing scheme based on the approximation of the maximum eigenvalue using state of the art results from Random Matrix Theory (RMT). The proposed sensing scheme has been evaluated through extensive simulations on wireless microphone signals and the proposed scheme shows higher probability of detection (Pd) performance. The proposed spectrum sensing also shows higher detection performance as compared to energy detection scheme and RMT based sensing schemes such as MME and EME.

We develop efficient coordination techniques that support inelastic traffic in large-scale distributed dynamic spectrum access DSA networks. By means of any learning algorithm, the proposed techniques enable DSA users to locate and exploit spectrum opportunities effectively, thereby increasing their achieved throughput (or "rewards" to be more general). Basically, learning algorithms allow DSA users to learn by interacting with the environment, and use their acquired knowledge to select the proper actions that maximize their own objectives, thereby "hopefully" maximizing their long-term cumulative received reward/throughput. However, when DSA users' objectives are not carefully coordinated, learning algorithms can lead to poor overall system performance, resulting in lesser per-user average achieved rewards. In this thesis, we derive efficient objective functions that DSA users an aim to maximize, and that by doing so, users' collective behavior also leads to good overall system performance, thus maximizing each user's long-term cumulative received rewards. We show that the proposed techniques are: (i) efficient by enabling users to achieve high rewards, (ii) scalable by performing well in systems with a small as well as a large number of users, (iii) learnable by allowing users to reach up high rewards very quickly, and (iv) distributive by being implementable in a decentralized manner. / Graduation date: 2013

The objective of the research is to develop analog spectrum processing techniques for cognitive radio (CR) applications in CMOS technology. CR systems aim to use the unoccupied spectrum allocations without any license when the primary users are not present. Therefore, the successful deployment of CR systems relies on their ability to accurately sense the spectrum usage status over a wide frequency range serving various wireless communication standards. Meanwhile, to maximize the utilization of the available spectrum segments, the bandwidth of the signal has to be highly flexible, so that even a small fraction of spectrum resources can be fully utilized by CR users. One of the key enabling technologies of variable bandwidth communication is a tunable baseband filter. In this research, a reconfigurable CR testbed system is presented as groundwork for the researches related with CR systems. With the feasibility study on the multi-resolution spectrum sensing (MRSS) functionality, a method for determining sensing threshold for MRSS functionality is presented, and a fully integrated MRSS receiver in CMOS technology is demonstrated. On the other hand, a reconfigurable CMOS analog baseband filter which can change its bandwidth, type and order with high resolution for CR applications is presented. In sum, an analog spectrum sensing method as well as a highly flexible analog baseband filter architecture is established and implemented in CMOS technology. Both designs are targeting the utilization of the analog signal processing capability with the aid of the digital circuits.

This thesis addresses the problem of building a Cognitive Radio that has the ability to interact with human users in a better way by making use of Quality of Experience (QoE) as its basis and marshalling its resources optimally around the user. Salient activities of this thesis include: Analysis of CR leads to the definition of its basic functional blocks such as cognition, learning and adaptation of radio behaviour in a multi-disciplinary manner. CR tracts signal processing for radio and sensors, cognitive and behavioural psychology for user intelligence, machine learning and AI for decision systems and optimization etc. Therefore it provides a rich, fertile area to make lateral connections across diverse helds. This thesis proposes a broad definition for CR (called as Unifed Cognitive Radio) inspired by key foundation works described in literature. Besides, it also describes its functionality and its ecosystem. Taking cue from the definition of UCR, this thesis proposes architectural frame-works for various sub-systems. Also their design and implementation is achieved with the aid of a comprehensive tested setup and is tested using realistic scenarios. Builds a set of intelligent decision systems that help to achieve the set goal. This involves various design decisions with a set of diverse algorithms from the world of signal processing, machine learning and articial intelligence. Transitioning disparate small functional entities (mostly built around experiments) into an integrated system that works in real-world environment is the key aspect of this thesis. It is definitely a challenging task. Therefore, starting from deterring the architectural reference frameworks for realizing various sub-systems of UCR to an evaluation based on integrated scenario, this being an important final step constitutes a sign cant amount of work. Analysis and implementation of the integrated system to meet the desired end functionality - QoE centricity of the CR system to satisfy the needs of the end user better is the contribution of this thesis

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.

Relay-assisted cooperative communication exploits spatial diversity to combat wireless fading, and is an appealing technology for next generation wireless systems. Several relay cooperation protocols have been proposed in the literature. In amplify-and-forward (AF)relaying, which is the focus of this thesis, the relay amplifies the signal it receives from the source and forwards it to the destination. AF has been extensively studied in the literature on account of its simplicity since the relay does not need to decode the received signal. We propose a novel optimal relaying policy for two-hop AF cooperative relay systems. In this, an average power-constrained relay adapts its gain and transmit power to minimize the fading-averaged symbol error probability (SEP) at the destination. Next, we consider a generalization of the above policy in which the relay operates as an underlay cognitive radio (CR). This mode of communication is relevant because it promises to address the spectrum shortage constraint. Here, the relay adapts its gain as a function of its local channel gain to the source and destination and also the primary such that the average interference it causes to the primary receiver is also constrained. For both the above policies, we also present near-optimal, simpler relay gain adaptation policies that are easy to implement and that provide insights about the optimal policies. The SEPs and diversity order of the policies are analyzed to quantify their performance. These policies generalize the conventional fixed-power and fixed-gain AF relaying policies considered in cooperative and CR literature, and outperform them by 2.0-7.7 dB. This translates into significant energy savings at the source and relay, and motivates their use in next generation wireless systems.

In this thesis the multi-polarized Cognitive Radios are studied. Cognitive Radios are proposed as an interesting way to more efficiently use the frequency resources. A Cognitive Radio secondary user finds the frequency bands which are not utilized by primary users and communicates on them without interfering with the primary users. In order to achieve this goal the secondary user must be able to detect reliably and quickly the presence of a primary user in a frequency band. In this thesis, the impact of polarization on the spectrum sensing performances of cognitive radio systems is studied.<p><p>First the depolarization occurring in the wireless channel is studied for two cognitive radio scenarios. This is done through an extensive measurement campaign in two outdoor-to-indoor and indoor-to-indoor scenarios where the parameters characterizing the radiowaves polarization are characterized at three different spatial scales: small-scale variation, large-scale variation and distance variation. <p><p>Second, a new approach is proposed in modeling of multi-polarized channels. The polarization of received fields is characterized from an electromagnetic point of view by modeling the polarization ellipse. Theoretical formulations are proposed in order to obtain the parameters characterizing the polarization ellipse based on the signals received on three cross-polarized antennas. A system-based statistical model of the time-dynamics of polarization is proposed based on an indoor-to-indoor measurement campaign. The analytical formulations needed in order to project the polarization ellipse onto a polarized multi-antenna system are given and it is shown how the model can be generated. <p><p>Third, the impact of polarization on the spectrum sensing performances of energy detection method is presented and its importance is highlighted. The performance of spectrum sensing with multi-polarized antennas is compared with unipolar single and multi-antenna systems. This analysis is based on an analytical formulation applied to the results obtained from the multi-polarized measurement campaign. The detection probability as a function of distance between the primary transmitter and the secondary terminal and the inter-antenna correlation effect on the spectrum sensing performance are studied. <p><p>An important limitation of energy detector is its dependence on the knowledge of the noise variance. An uncertainty on the estimation of the noise variance considerably affects the performance of energy detector. This limitation is resolved by proposing new multi-polarized spectrum sensing methods which do not require any knowledge neither on the primary signal nor on the noise variance. These methods, referred to as “Blind spectrum sensing methods”, are based on the use of three cross-polarized antennas at the secondary terminal. Based on an analytical formulation and the results obtained from the measurement campaign, the performances of the proposed methods are compared with each-other and with the energy detection method. The effect of antenna orientation on the spectrum sensing performance of the proposed methods and the energy detection method is studied using the proposed elliptical polarization model. <p> / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

The objective of the proposed research is to develop interference-aware resource management techniques for CR networks that opportunistically operate within the licensed primary networks spectrum and to investigate the application of such CR techniques to emerging wireless networks. In this thesis, we report on a set of laboratory experiments that we undertook to analyze the interference between the CR-based wireless regional-area network (WRAN) standard and the digital television (DTV) broadcasting system. We determined the tolerable levels of WRAN interference into DTV receivers and studied the effect of these interference levels on WRAN deployment. Based on the need for efficient utilization of the primary network spectrum, we propose efficient interference-aware radio resource allocation (RRA) techniques for orthogonal frequency-division multiple access (OFDMA) CR networks. These RRA techniques aim to maximize the CR network throughput and to keep the CR interference to the primary network at or below a predefined threshold, known as the "interference temperature" limit. Moreover, we propose a joint spectrum-sensing design and power control algorithm that lead to increased CR network throughput and efficient protection of the PUs from undue interference. Interference coordination (IC) is considered a key technique for capacity maximization in emerging heterogeneous wireless networks. We propose a CR-based IC and RRA algorithm for OFDMA femtocell deployments to achieve efficient spectrum utilization and maximum network throughput. CR is envisioned as a key enabling technology for future wireless networks; our novel CR techniques will provide other researchers useful tools to design such networks.