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.

The rapid increasing interest in wireless communication has led to the continuous development of wireless devices and technologies. The modern convergence and interoperability of wireless technologies has further increased the amount of services that can be provided, leading to the substantial demand for access to the radio frequency spectrum in an efficient manner. Cognitive radio (CR) an innovative concept of reusing licensed spectrum in an opportunistic manner promises to overcome the evident spectrum underutilization caused by the inflexible spectrum allocation. Spectrum sensing in an unswerving and proficient manner is essential to CR. Cooperation amongst spectrum sensing devices are vital when CR systems are experiencing deep shadowing and in a fading environment. In this thesis, cooperative spectrum sensing (CSS) schemes have been designed to optimize detection performance in an efficient and implementable manner taking into consideration: diversity performance, detection accuracy, low complexity, and reporting channel bandwidth reduction. The thesis first investigates state of the art spectrums sensing algorithms in CR. Comparative analysis and simulation results highlights the different pros, cons and performance criteria of a practical CSS scheme leading to the problem formulation of the thesis. Motivated by the problem of diversity performance in a CR network, the thesis then focuses on designing a novel relay based CSS architecture for CR. A major cooperative transmission protocol with low complexity and overhead - Amplify and Forward (AF) cooperative protocol and an improved double energy detection scheme in a single relay and multiple cognitive relay networks are designed. Simulation results demonstrated that the developed algorithm is capable of reducing the error of missed detection and improving detection probability of a primary user (PU). To improve spectrum sensing reliability while increasing agility, a CSS scheme based on evidence theory is next considered in this thesis. This focuses on a data fusion combination rule. The combination of conflicting evidences from secondary users (SUs) with the classical Dempster Shafter (DS) theory rule may produce counter-intuitive results when combining SUs sensing data leading to poor CSS performance. In order to overcome and minimise the effect of the counter-intuitive results, and to enhance performance of the CSS system, a novel state of the art evidence based decision fusion scheme is developed. The proposed approach is based on the credibility of evidence and a dissociability degree measure of the SUs sensing data evidence. Simulation results illustrate the proposed scheme improves detection performance and reduces error probability when compared to other related evidence based schemes under robust practcial scenarios. Finally, motivated by the need for a low complexity and minmum bandwidth reporting channels which can be significant in high data rate applications, novel CSS quantization schemes are proposed. Quantization methods are considered for a maximum likelihood estimation (MLE) and an evidence based CSS scheme. For the MLE based CSS, a novel uniform and optimal output entropy quantization scheme is proposed to provide fewer overhead complexities and improved throughput. While for the Evidence based CSS scheme, a scheme that quantizes the basic probability Assignment (BPA) data at each SU before being sent to the FC is designed. The proposed scheme takes into consideration the characteristics of the hypothesis distribution under diverse signal-to-noise ratio (SNR) of the PU signal based on the optimal output entropy. Simulation results demonstrate that the proposed quantization CSS scheme improves sensing performance with minimum number of quantized bits when compared to other related approaches.

In this thesis we investigate how to improve the performance of MU-MIMO wireless system in terms of achieving Shannon capacity limit and efficient use of precious resource of radio spectrum in wireless communication. First a new suboptimal volume-based scheduling algorithm is presented, which can be applied in MU-MIMO downlink system to transmit signals concurrently to multiple users under the assumption of perfect channel information at transmitter and receiver. The volume-based scheduling algorithm utilises Block Diagonalisation precoding and Householder reduction procedure of QR factorisation. In comparison with capacity-based suboptimal scheduling algorithm, the volume-based algorithm has much reduced computational complexity with only a fraction of sum-rate capacity penalty from the upper bound of system capacity limit. In comparison with semi-orthogonal user selection suboptimal scheduling algorithm, the volume-based scheduling algorithm can be implemented with less computational complexity. Furthermore, the sum-rate capacity achieved via volume-based scheduling algorithm is higher than that achieved by SUS scheduling algorithm in the MIMO case. Then, a two-step scheduling algorithm is proposed, which can be used in the MU-MIMO system and under the assumption that channel state information is known to the receiver, but it is not known to the transmitter and the system under the feedback resource constraint. Assume that low bits codebook and high bits codebook are stored at the transmitter and receiver. The users are selected by using the low bits codebook; subsequently the BD precoding vectors for selected users are designed by employing high bits codebook. The first step of the algorithm can alleviate the load on feedback uplink channel in the MU-MIMO wireless system while the second step can aid precoding design to improve system sum-rate capacity. Next, a MU-MIMO cognitive radio (CR) wireless system has been studied. In such system, a primary wireless network and secondary wireless network coexist and the transmitters and receivers are equipped with multiple antennas. Spectrum sensing methods by which a portion of spectrum can be utilised by a secondary user when the spectrum is detected not in use by a primary user were investigated. A Free Probability Theory (FPT) spectrum sensing method that is a blind spectrum sensing method is proposed. By utilizing the asymptotic behaviour of random matrix based on FPT, the covariance matrix of transmitted signals can be estimated through a large number of observations of the received signals. The method performs better than traditional energy spectrum sensing method. We also consider cooperative spectrum sensing by using the FPT method in MU-MIMO CR system. Cooperative spectrum sensing can improve the performance of signal detection. Furthermore, with the selective cooperative spectrum sensing approach, high probability of detection can be achieved when the system is under false alarm constraint. Finally, spectrum sensing method based on the bispectrum of high-order statistics (HOS) and receive diversity in SIMO CR system is proposed. Multiple antennas on the receiver can improve received SNR value and therefore enhance spectrum sensing performance in terms of increase of system-level probability of detection. Discussions on cooperative spectrum sensing by using the spectrum sensing method based on HOS and receive diversity are presented.

The continuing increase in the adoption and use of wireless technology aggravates the problem of spectrum scarcity due to the way we utilize the spectrum. The radio spectrum is a limited resource regulated by governmental agencies according to a fixed spectrum assignment policy. However, many studies show that this fixed radio frequency allocation leads to significant underutilization of the radio spectrum creating artificial scarcity, as most of the allocated spectrum is not used all of the time in every location. To meet services growing demands, efficient use of the spectrum is essential. Therefore, there is a need to estimate the radio spectrum utilization in several locations and during different periods of time in order to opportunistically exploit the existing wireless spectrum. Cognitive radio technology aims to search for those portions of the radio spectrum that are assigned to a specific service, but are unused during a specific time and at specific location in order to share these white spaces and thus to reduce the radio spectrum inefficiency. In this thesis, we study spectrum utilization in the frequency range from 790MHz to 925MHz. The spectrum sensing has been realized using a number of specialized microcontroller based white space sensors which utilize energy detection, situated in different locations of a building in Kista, Sweden. The occupancy of the frequency bands in this chunk of the spectrum is quantified as the fraction of samples with a power level greater than a threshold. The results from these spectrum measurements show that a significant amount of spectrum in this scanned range around the building is inefficiently used all the time. / Den senaste tidens ökning av trådlös teknik förvärrar problemet med spektrumbrist på grund av hur vi använder den. Det radiospektrum är en begränsad resurs som regleras av statliga myndigheter enligt en fast spektrumtilldelningen politik. Men många studier visar att den fasta frekvensplan leder till betydande underutnyttjande av radiospektrum och skapar en konstgjord brist eftersom de flesta av de tilldelade spektrumet inte används hela tiden i varje platsen. För att uppfylla tjänster ökade krav, är viktigt en effektiv användning av spektrumet. Därför finns det ett behov av att uppskatta användningen av radiospektrum i flera platser och i olika tidsperioder för att kunna utnyttja den befintliga trådlösa spektrumet i opportunistiskt sätt. Kognitiv radio teknologi syftar till att söka efter dessa delar av radiospektrum som tilldelas till en konkret tjänst och är oanvända i en viss tid och på viss plats för att dela dessa vita ytor och därför lösa radio spektrum ineffektivitet problem. I denna uppsats studerar vi spektrumanvändning i frekvensområdet från 790 MHz till 925 MHz. Spektrat avkänning har utförts med hjälp av ett antal specialiserade mikrokontroller blanktecken sensorer vilka utnyttjar energin upptäckt, som ligger på olika platser i en byggnad i Kista, Sverige. Uthyrningsgraden av frekvensbanden i denna del av spektrumet kvantifieras som antalet prover med en effektnivå överstiger en tröskel. Resultaten från spektrat mätningarna visar att en betydande del av spektrumet i denna scannade intervall ineffektivt används hela tiden.

This research considers the problem of cooperatively identifying the active bands in a wideband spectrum using the sparse Fast Fourier Transform (sFFT). Existing research has focused primarily on Compressed Sensing (CS) and Multi-Coset (MC) sampling, but recent developments in the sFFT have shown that a sparsely occupied spectrum can be efficiently reconstructed using multiple co-prime analog-to-digital converters (ADC) that sample below the Nyquist rate. Specifically, this research utilizes the Robust Fast Fourier Aliasing-based Sparse Transform (R-FFAST) and extends this algorithm for use in cooperative wideband spectrum sensing (CWSS). Unlike previous approaches that implement the sFFT for spectrum sensing, the R-FFAST framework was developed and analyzed using the mutual coherence and the restricted isometry property (RIP) from CS theory. This leads to reliable support estimation in the presence of additive white Gaussian noise (AWGN) while mitigating the computational complexity of CS reconstruction algorithms. This research makes the following contributions. First, this research extends the signal model from single tones to multi-band signals with clustered support. Second, it shows that each stage in the R-FFAST front-end can be decomposed into individual nodes that form a fully distributed cooperative network. Lastly, this research empirically develops a constant false alarm rate (CFAR) detector that is used to identify the active frequency bins during the reconstruction process. The primary result of this research is showing that reliable spectrum detection is only possible when the average sampling rate of the cooperative network is greater than or equal to the sparsity of the spectrum. Simulation results are provided to demonstrate the effectiveness of the proposed framework and validate the findings of this research. / Electrical and Computer Engineering

We investigate spectral-efficient design and develop novel schemes to improve spectral efficiency of the modern wireless communications networks. Nowadays, more and more spectrum resources are required to support various high-data-rate applications while spectrum resources are limited. Moreover, static allocation and exclusive access in current spectrum assignment policy caused a lot of licensed spectrum bands to be underutilized. To deal with the problem, cognitive radio (CR) has been developed, which allows unlicensed/secondary users to transmit with licensed/primary users as long as the former ones do not generate intolerable interference to the latter ones. The coexistence of users and networks requires careful and dynamic planning to mitigate interference. Otherwise, the network performance will be severely undermined. We study both spectrum sensing and spectrum access techniques and propose several transmit schemes for different types of cognitive ratio networks, including spectrum overlay and spectrum underlay systems. The proposed algorithms can improve spectral efficiency of the networks efficiently and have potentials to be used in future wireless communications networks.

The term of Cognitive Radio (CR) used to indicate that spectrum radio could be accessed dynamically and opportunistically by unlicensed users. In CR Networks, Interference between nodes, hidden terminal problem, and spectrum sensing errors are big issues to be widely discussed in the research field nowadays. To improve the performance of such kind of networks, this thesis proposes Cognitive Medium Access Control (MAC) protocols for Mobile Ad-Hoc Networks (MANETs). From the concept of CR, this thesis has been able to develop a cognitive MAC framework in which a cognitive process consisting of cognitive elements is considered, which can make efficient decisions to optimise the CR network. In this context, three different scenarios to maximize the secondary user's throughput have been proposed. We found that the throughput improvement depends on the transition probabilities. However, considering the past information state of the spectrum can dramatically increases the secondary user's throughput by up to 40%. Moreover, by increasing the number of channels, the throughput of the network can be improved about 25%. Furthermore, to study the impact of Physical (PHY) Layer errors on cognitive MAC layer in MANETs, in this thesis, a Sensing Error-Aware MAC protocols for MANETs has been proposed. The developed model has been able to improve the MAC layer performance under the challenge of sensing errors. In this context, the proposed model examined two sensing error probabilities: the false alarm probability and the missed detection probability. The simulation results have shown that both probabilities could be adapted to maintain the false alarm probability at certain values to achieve good results. Finally, in this thesis, a cooperative sensing scheme with interference mitigation for Cognitive Wireless Mesh Networks (CogMesh) has been proposed. Moreover, a prioritybased traffic scenario to analyze the problem of packet delay and a novel technique for dynamic channel allocation in CogMesh is presented. Considering each channel in the system as a sub-server, the average delay of the users' packets is reduced and the cooperative sensing scenario dramatically increases the network throughput 50% more as the number of arrival rate is increased.

Cognitive Radio Networks (CRNs) are a next generation network that is expected to solve the wireless spectrum shortage problem, which is the shrinking of available wireless spectrum resources needed to facilitate future wireless applications. The first CRN standard, the IEEE 802.22, addresses this particular problem by allowing CRNs to share geographically unused TV spectrum to mitigate the spectrum shortage. Equipped with reasoning and learning engines, cognitive radios operate autonomously to locate unused channels to maximize its own bandwidth and Quality-of-Service (QoS). However, their increased capabilities over traditional radios introduce a new dimension of security threats. In an NSF 2009 workshop, the FCC raised the question, “What authentication mechanisms are needed to support cooperative cognitive radio networks? Are reputation-based schemes useful supplements to conventional Public Key Infrastructure (PKI) authentication protocols?” Reputation-based schemes in cognitive radio networks are a popular technique for performing robust and accurate spectrum sensing without any inter-communication with licensed networks, but the question remains on how effective they are at satisfying the FCC security requirements. Our work demonstrates that trust-based Cooperative Spectrum Sensing (CSS) protocols are vulnerable to rogue signals, which creates the illusion of inside attackers and raises the concern that such schemes are overly sensitive Intrusion Detection Systems (IDS). The erosion of the sensor reputations in trust-based CSS protocols makes CRNs vulnerable to future attacks. To counter this new threat, we introduce community detection and cluster analytics to detect and negate the impact of rogue signals on sensor reputations.

La thèse aborde le problème de la détection d’un signal dans une bande de fréquences donnée sans aucune connaissance à priori sur la source (détection aveugle) dans le contexte de la radio intelligente. Le détecteur proposé dans la thèse est basé sur l’estimation des valeurs propres de la matrice de corrélation du signal reçu. A partir de ces valeurs propres, plusieurs critères ont été développés théoriquement (Standard Condition Number, Scaled Largest Eigenvalue, Largest Eigenvalue) en prenant pour hypothèse majeure un nombre fini d’éléments, contrairement aux hypothèses courantes de la théorie des matrices aléatoires qui considère un comportement asymptotique de ces critères. Les paramètres clés des détecteurs ont été formulés mathématiquement (probabilité de fausse alarme, densité de probabilité) et une correspondance avec la densité GEV a été explicitée. Enfin, ce travail a été étendu au cas multi-antennes (MIMO) pour les détecteurs SLE et SCN. / In Cognitive Radio, Spectrum Sensing (SS) is the task of obtaining awareness about the spectrum usage. Mainly it concerns two scenarios of detection: (i) detecting the absence of the Primary User (PU) in a licensed spectrum in order to use it and (ii) detecting the presence of the PU to avoid interference. Several SS techniques were proposed in the literature. Among these, Eigenvalue Based Detector (EBD) has been proposed as a precious totally-blind detector that exploits the spacial diversity, overcome noise uncertainty challenges and performs adequately even in low SNR conditions. The first part of this study concerns the Standard Condition Number (SCN) detector and the Scaled Largest Eigenvalue (SLE) detector. We derived exact expressions for the Probability Density Function (PDF) and the Cumulative Distribution Function (CDF) of the SCN using results from finite Random Matrix Theory; In addition, we derived exact expressions for the moments of the SCN and we proposed a new approximation based on the Generalized Extreme Value (GEV) distribution. Moreover, using results from the asymptotic RMT we further provided a simple forms for the central moments of the SCN and we end up with a simple and accurate expression for the CDF, PDF, Probability of False-Alarm, Probability of Detection, of Miss-Detection and the decision threshold that could be computed and hence provide a dynamic SCN detector that could dynamically change the threshold value depending on target performance and environmental conditions. The second part of this study concerns the massive MIMO technology and how to exploit the large number of antennas for SS and CRs. Two antenna exploitation scenarios are studied: (i) Full antenna exploitation and (ii) Partial antenna exploitation in which we have two options: (i) Fixed use or (ii) Dynamic use of the antennas. We considered the Largest Eigenvalue (LE) detector if noise power is perfectly known and the SCN and SLE detectors when noise uncertainty exists.

Reliable wireless networks for high speed trains require a significant amount of data communications for enabling safety features such as train collision avoidance and railway management. Cognitive radio integrates heterogeneous wireless networks that will be deployed in order to achieve intelligent communications in future railway systems. One of the primary technical challenges in achieving reliable communications for railways is the handling of high mobility environments involving trains, which includes significant Doppler shifts in the transmission as well as severe fading scenarios that makes it difficult to estimate wireless spectrum utilization. This thesis has two primary contributions: (1) The creation of a Heterogeneous Cooperative Spectrum Sensing (CSS) prototype system, and (2) the derivation of a Long Term Evolution for Railways (LTE-R) system performance analysis. The Heterogeneous CSS prototype system was implemented using Software-Defined Radios (SDRs) possessing different radio configurations. Both soft and hard-data fusion schemes were used in order to compare the signal source detection performance in real-time fading scenarios. For future smart railways, one proposed solution for enabling greater connectivity is to access underutilized spectrum as a secondary user via the dynamic spectrum access (DSA) paradigm. Since it will be challenging to obtain an accurate estimate of incumbent users via a single-sensor system within a real-world fading environment, the proposed cooperative spectrum sensing approach is employed instead since it can mitigate the effects of multipath and shadowing by utilizing the spatial and temporal diversity of a multiple radio network. Regarding the LTE-R contribution of this thesis, the performance analysis of high speed trains (HSTs) in tunnel environments would provide valuable insights with respect to the smart railway systems operating in high mobility scenarios in drastically impaired channels.