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.

Faced with imminent spectrum scarcity largely due to inflexible licensed band arrangements, cognitive radio (CR) has been proposed to facilitate higher spectrum utilization by allowing cognitive users (CUs) to access the licensed bands without causing harmful interference to primary users (PUs). To achieve this without the aid of PUs, the CUs have to perform spectrum sensing reliably detecting the presence or absence of PU signals. Without reliable spectrum sensing, the discovery of spectrum opportunities will be inefficient, resulting in limited utilization enhancement. This dissertation examines three major techniques for spectrum sensing, which are matched filter, energy detection, and cyclostationary feature detection. After evaluating the advantages and disadvantages of these techniques, we narrow down our research to a focus on cyclostationary feature detection (CFD). Our first contribution is to boost performance of an existing and prevailing CFD method. This boost is achieved by our proposed optimal and sub-optimal schemes for identifying best hypothesis test points. The optimal scheme incorporates prior knowledge of the PU signals into test point selection, while the sub-optimal scheme circumvents the need for this knowledge. The results show that our proposed can significantly outperform other existing schemes. Secondly, in view of multi-antenna deployment in CR networks, we generalize the CFD method to include the multi-antenna case. This requires effort to justify the joint asymptotic normality of vector-valued statistics and show the consistency of covariance estimates. Meanwhile, to effectively integrate the received multi-antenna signals, a novel cyclostationary feature based channel estimation is devised to obtain channel side information. The simulation results demonstrate that the errors of channel estimates can diminish sharply by increasing the sample size or the average signal-to-noise ratio. In addition, no research has been found that analytically assessed CFD performance over fading channels. We make a contribution to such analysis by providing tight bounds on the average detection probability over Nakagami fading channels and tight approximations of diversity reception performance subject to independent and identically distributed Rayleigh fading. For successful coexistence with the primary system, interference management in cognitive radio networks plays a prominent part. Normally certain average or peak transmission power constraints have to be placed on the CR system. Depending on available channel side information and fading types (fast or slow fading) experienced by the PU receiver, we derive the corresponding constraints that should be imposed. These constraints indicate that the second moment of interference channel gain is an important parameter for CUs allocating transmission power. Hence, we develop a cooperative estimation procedure which provides robust estimate of this parameter based on geolocation information. With less aid from the primary system, the success of this procedure relies on statistically correlated channel measurements from cooperative CUs. The robustness of our proposed procedure to the uncertainty of geolocation information is analytically presented. Simulation results show that this procedure can lead to better mean-square error performance than other existing estimates, and the effects of using inaccurate geolocation information diminish steadily with the increasing number of cooperative cognitive users.

The scope of this work is to map both conventional and less conventional methods of signal detection in the radio channel, computer simulation of selected methods and subsequent implementation selected method (algorithm) to FPGA chip.

Efficient utilization of frequency bands has attracted more and more attention. Most of the licensed spectrum nowadays is under-utilized and some unlicensed services are allowed to use the available spectrum without causing harmful interference to the primary users. Therefore, unlicensed users should be able to detect spectrum holes reliably. Spectrum sensing and estimation is an important factor to achieve this. In this thesis, several spectrum sensing and estimation methods are compared based on receiver operating characteristics. Simulation results show that there is a trade-off among different methods.

Cognitive radio networks (CRNs) have emerged as a solution for the looming spectrum crunch caused by the rapid adoption of wireless devices over the previous decade. This technology enables efficient spectrum utility by dynamically reusing existing spectral bands. A CRN achieves this by requiring its users – called secondary users (SUs) – to measure and opportunistically utilise the band of a legacy broadcaster – called a primary user (PU) – in a process called spectrum sensing. Sensing requires the distribution and fusion of measurements from all SUs, which is facilitated by a variety of architectures and topologies. CRNs possessing a central computation node are called centralised networks, while CRNs composed of multiple computation nodes are called decentralised networks. While simpler to implement, centralised networks are reliant on the central node – the entire network fails if this node is compromised. In contrast, decentralised networks require more sophisticated protocols to implement, while offering greater robustness to node failure. Relay-based networks, a subset of decentralised networks, distribute the computation over a number of specialised relay nodes – little research exists on spectrum sensing using these networks. CRNs are vulnerable to unique physical layer attacks targeted at their spectrum sensing functionality. One such attack is the Byzantine attack; these attacks occur when malicious SUs (MUs) alter their sensing reports to achieve some goal (e.g. exploitation of the CRN’s resources, reduction of the CRN’s sensing performance, etc.). Mitigation strategies for Byzantine attacks vary based on the CRN’s network architecture, requiring defence algorithms to be explored for all architectures. Because of the sparse literature regarding relay-based networks, a novel algorithm – suitable for relay-based networks – is proposed in this work. The proposed algorithm performs joint MU detection and secure sensing by large-scale probabilistic inference of a statistical model. The proposed algorithm’s development is separated into the following two parts. • The first part involves the construction of a probabilistic graphical model representing the likelihood of all possible outcomes in the sensing process of a relay-based network. This is done by discovering the conditional dependencies present between the variables of the model. Various candidate graphical models are explored, and the mathematical description of the chosen graphical model is determined. • The second part involves the extraction of information from the graphical model to provide utility for sensing. Marginal inference is used to enable this information extraction. Belief propagation is used to infer the developed graphical model efficiently. Sensing is performed by exchanging the intermediate belief propagation computations between the relays of the CRN. Through a performance evaluation, the proposed algorithm was found to be resistant to probabilistic MU attacks of all frequencies and proportions. The sensing performance was highly sensitive to the placement of the relays and honest SUs, with the performance improving when the number of relays was increased. The transient behaviour of the proposed algorithm was evaluated in terms of its dynamics and computational complexity, with the algorithm’s results deemed satisfactory in this regard. Finally, an analysis of the effectiveness of the graphical model’s components was conducted, with a few model components accounting for most of the performance, implying that further simplifications to the proposed algorithm are possible. / Dissertation (MEng)--University of Pretoria, 2020. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

Cognitive radios is one of the hot topics for emerging and future wireless commu- nication. It has been proposed as a suitable solution for the spectrum scarcity caused by the increase in frequency demand. The concept is based on allowing unlicensed users, called cognitive or secondary users, to share the unoccupied frequency bands with their owners, called the primary users, under constraints on the interference they cause to them. The objective of our work is to propose some enhancements to cognitive radio systems while taking into account practical constraints. Cogni- tive radios requires a capability to detect spectrum holes (spectrum sensing) and a scheduling flexibility to avoid the occupied spectrum and selectively use the empty spectrum (dynamic resource allocation). Thus, the work is composed of two main parts. The first part focuses on cooperative spectrum sensing. We compute in this part the analytical performance of cooperative spectrum sensing under non identical and imperfect channels. Different schemes are considered for the cooperation between users such as hard binary, censored information, quantized, and soft information. The second part focuses on the dynamic resource allocation. We first propose low-cost re- source allocation algorithms that use location information to estimate the interference to primary users to replace absence of instantaneous channel state information. We extend these algorithms to handle practical implementation constraints such as dis- 5 crete bit-loading and collocated subcarriers allocations. We then propose a reduced dimension approach based on the grouping of subcarriers into clusters and performing the resource allocation over clusters of subcarriers instead of single subcarriers. This approach is shown to reduce the computational complexity of the algorithm with lim- ited performance loss. In addition, it is valid for a generic set of resource allocation problems in presence of co-channel interference between users.

The current and future trends of modern wireless communication systems place heavy demands on fast data transmissions in order to satisfy end users’ requirements anytime, anywhere. Such demands are obvious in recent applications such as smart phones, long term evolution (LTE), 4 & 5 Generations (4G & 5G), and worldwide interoperability for microwave access (WiMAX) platforms, where robust coding and modulations are essential especially in streaming on-line video material, social media and gaming. This eventually resulted in extreme exhaustion imposed on the frequency spectrum as a rare natural resource due to stagnation in current spectrum management policies. Since its advent in the late 1990s, cognitive radio (CR) has been conceived as an enabling technology aiming at the efficient utilisation of frequency spectrum that can lead to potential direct spectrum access (DSA) management. This is mainly attributed to its internal capabilities inherited from the concept of software defined radio (SDR) to sniff its surroundings, learn and adapt its operational parameters accordingly. CR systems (CRs) may commonly comprise one or all of the following core engines that characterise their architectures; namely, adaptive coded modulation (ACM), automatic modulation classification (AMC) and spectrum sensing (SS). Motivated by the above challenges, this programme of research is primarily aimed at the design and development of new paradigms to help improve the adaptability of CRs and thereby achieve the desirable signal processing tasks at the physical layer of the above core engines. Approximate modelling of Rayleigh and finite state Markov channels (FSMC) with a new concept borrowed from econometric studies have been approached. Then insightful channel estimation by using Kalman filter (KF) augmented with interacting multiple model (IMM) has been examined for the purpose of robust adaptability, which is applied for the first time in wireless communication systems. Such new IMM-KF combination has been facilitated in the feedback channel between wireless transmitter and receiver to adjust the transmitted power, by using a water-filling (WF) technique, and constellation pattern and rate in the ACM algorithm. The AMC has also benefited from such IMM-KF integration to boost the performance against conventional parametric estimation methods such as maximum likelihood estimate (MLE) for channel interrogation and the estimated parameters of both inserted into the ML classification algorithm. Expectation-maximisation (EM) has been applied to examine unknown transmitted modulation sequences and channel parameters in tandem. Finally, the non-parametric multitaper method (MTM) has been thoroughly examined for spectrum estimation (SE) and SS, by relying on Neyman-Pearson (NP) detection principle for hypothesis test, to allow licensed primary users (PUs) to coexist with opportunistic unlicensed secondary users (SUs) in the same frequency bands of interest without harmful effects. The performance of the above newly suggested paradigms have been simulated and assessed under various transmission settings and revealed substantial improvements.

No / Opportunistic secondary usage of underutilised radio spectrum is currently of great interest and the use of TV White Spaces (TVWS) has been considered for Long Term Evolution (LTE) broadband services. However, wireless microphones operating in TV bands pose a challenge to TVWS opportunistic access. Efficient and proactive spectrum sensing could prevent harmful interference between collocated devices, but existing spectrum sensing schemes such as energy detection and schemes based on Random Matrix Theory (RMT) have performance limitations. We propose a new blind spectrum sensing scheme with higher performance based on RMT supported by a new formula for the estimation of noise variance. The performance of the proposed scheme has been evaluated through extensive simulations on wireless microphone signals. The proposed scheme has also been compared to energy detection schemes, and shows higher performance in terms of the probability of false alarm (Pfa) and probability of detection (Pd).

The spectacular growth in wireless services imposes scarcity in term of the available radio spectrum. A solution to overcome this scarcity is to adopt what so called cognitive radio based on dynamic spectrum access. With dynamic spectrum access, secondary (unlicensed) users can access  spectrum owned by primary (licensed) users when it is temporally and/or geographically unused. This unused spectrum is termed as spectrum opportunity. Finding these spectrum opportunities related aspects are studied in this thesis where two approaches of finding spectrum opportunities, namely spectrum sensing and geo-locations databases are considered. In spectrum sensing arena, two topics are covered, blind spectrum sensing and sensing time and periodic sensing interval optimization. For blind spectrum sensing, a spectrum scanner based on maximum minimum eigenvalues detector and frequency domain rectangular filtering is developed. The measurements show that the proposed scanner outperforms the energy detector scanner in terms of the probability of detection. Continuing in blind spectrum sensing, a novel blind spectrum sensing technique based on discriminant analysis called spectrum discriminator has been developed in this thesis. Spectrum discriminator has been further developed to peel off multiple primary users with different transmission power from a wideband sensed spectrum. The spectrum discriminator performance is measured and compared with the maximum minimum eigenvalues detector in terms of the probability of false alarm, the probability of detection and the sensing time. For sensing time and periodic sensing interval optimization, a new approach that aims at maximizing the probability of right detection, the transmission efficiency and the captured opportunities is proposed and simulated. The proposed approach optimizes the sensing time and the periodic sensing interval iteratively. Additionally, the periodic sensing intervals for multiple channels are optimized to achieve as low sensing overhead and unexplored opportunities as possible for a multi channels system. The thesis considers radar bands and TV broadcasting bands to adopt geo-locations databases for spectrum opportunities. For radar bands, the possibility of spectrum sharing with secondary users in L, S and C bands is investigated. The simulation results show that band sharing is possible with more spectrum opportunities offered by C band than S and L band which comes as the least one. For the TV broadcasting bands, the thesis treats the power assignment for secondary users operate in Gävle area, Sweden. Furthermore, the interference that the TV transmitter would cause to the secondary users is measured in different locations in the same area. / <p>QC 20130114</p> / QUASAR

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