Redes de sensores sem fio tem ganhado grande destaque em diferentes aplicações, tais como, domésticas, comercial e industrial, trazendo mais flexibilidade e mais conveniência em nossas vidas. Entretanto, seu desempenho é influenciado por diversos fatores como, por exemplo, características do ambiente de propagação das ondas de rádio e outras tecnologias de comunicação sem fio que podem coexistir em uma mesma área de cobertura. Os recursos utilizados nas comunicações sem fio são limitados e muitas vezes não exclusivos possibilitando interferências provenientes de diferentes tipos de fontes. O presente trabalho busca soluções para o uso mais eficiente dos recursos da rede de comunicação sem fio, são investigados e propostos métodos adaptativos para uma rede sem fio industrial, o protocolo WirelessHART, utilizando mecanismos dinâmicos de sensoriamento de espectro e seleção de canal entre os dispositivos da rede. É apresentado uma arquitetura de gerenciamento do espectro em conformidade com o protocolo, baseado em sensoriamento do espectro distribuído e no monitoramento do desempenho das comunicações. A arquitetura utilizada permite a classificação de um conjunto de canais específicos entre os pares de dispositivos durante a operação da rede de comunicação. O trabalho demonstra o desempenho dos mecanismos desenvolvidos para a detecção de interferências com redes do tipo IEEE 802.11. / Wireless sensor networks have been expanding rapidly in many applications for different areas such as residential, office and industrial. Wireless connections bring many advantages as installation feasibility, scalability, mobility and reduce infrastructure costs. However, wireless network performance is affected by many factors as, for example, environment characteristics and other wireless communication technologies at the same coverage area. The wireless communication resources are limited and many times shared, allowing interferences from different kind of electromagnetic sources. This work presents a solution for an efficient use of the wireless communication network resources, investigate and propose adaptive methods for an industrial wireless network, the WirelessHART protocol, using dynamic mechanisms of spectrum sensing and channel selection between the devices. A protocol spectrum management architecture based on distributed sensing and monitoring of communications performance is presented, in compliance with WirelessHART protocol, allowing the classification of a set of specific channels between peer devices during the communication network’s operation. Also, it is presented the channel selection performance for IEEE 802.11 interference.

Radio Communication technologies are undergoing drastic demand over the past two decades. The precious radio resource, electromagnetic radio spectrum, is in vain as technology advances. It is required to come up with a solution to improve its wise uses. Cognitive Radio enabled by Software-Defined Radio brings an intelligent solution to efficiently use the Radio Spectrum. It is a method to aware the radio communication system to be able to adapt to its radio environment like signal power and free spectrum holes. The approach will pose a question on how to efficiently detect a signal. In this thesis different spectrum sensing algorithm will be explained and a special concentration will be on new sensing algorithm based on the Eigenvalues of received signal. The proposed method adapts blind signal detection approach for applications that lacks knowledge about signal, noise and channel property. There are two methods, one is ratio of the Maximum Eigenvalue to Minimum Eigenvalue and the second is ratio of Signal Power to Minimum Eigenvalue. Random Matrix theory (RMT) is a branch of mathematics and it is capable in analyzing large set of data or in a conclusive approach it provides a correlation points in signals or waveforms. In the context of this thesis, RMT is used to overcome both noise and channel uncertainties that are common in wireless communication. Simulations in MATLAB and real-time measurements in LabVIEW are implemented to test the proposed detection algorithms. The measurements were performed based on received signal from an IF-5641R Transceiver obtained from National Instruments.

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.

Ultrawideband and cognitive radio are two of the most important approaches that are shaping the future of wireless communication systems. At a first glance, the aims of UWB and cognitive radio do not seem to be overlapping significantly, however, there is a strong synergy between the capabilities of UWB and the goals of cognitive radio. One of the objectives of this thesis is to shed the first light on the marriage of these two important approaches.Ultrawideband (UWB) is a promising technology for future short-range, high-data rate wireless communication networks. Along with its exciting features including achieving high data rates, low transmission power requirement, and immunity to multipath effects, UWB is unique in its coexistence capability with narrowband systems.In this thesis, the details of practical UWB implementation are provided. Regarding the coexistence of UWB with licensed narrowband systems, narrowband interference (NBI)avoidance and cancelation techniques in the literature are investigated. It is aimed to emphasize that UWB is a strong candidate for cognitive radio, and this fact is proven by providing two different approaches in which ultrawideband is combined with cognitive radio to maximize the performance of unlicensed communications.

Traditionally radio spectrum was considered a commodity to be allocated in a fixed and centralized manner, but now the technical community and the regulators approach it as a shared resource that can be flexibly and intelligently shared between competing entities. In this thesis we focus on novel strategies to sense and access the radio spectrum within the framework of Opportunistic Spectrum Access via Cognitive Radio Networks (CRNs). In the first part we develop novel transmit opportunity detection methods that effectively exploit the gray space present in packet based networks. Our methods proactively detect the maximum safe transmit power that does not significantly affect the primary network nodes via an implicit feedback mechanism from the Primary network to the Secondary network. A novel use of packet interarrival duration is developed to robustly perform change detection in the primary network's Quality of Service. The methods are validated on real world IEEE 802.11 WLANs. In the second part we study the inferential use of Goodness-of-Fit tests for spectrum sensing applications. We provide the first comprehensive framework for decision fusion of an ensemble of goodness-of-fit tests through use of p-values. Also, we introduce a generalized Phi-divergence statistic to formulate goodness-of-fit tests that are tunable via a single parameter. We show that under uncertainty in the noise statistics or non-Gaussianity in the noise, the performance of such non-parametric tests is significantly superior to that of conventional spectrum sensing methods. Additionally, we describe a collaborative spatially separated version of the test for robust combining of tests in a distributed spectrum sensing setting. In the third part we develop the sequential energy detection problem for spectrum sensing and formulate a novel Sequential Energy Detector. Through extensive simulations we demonstrate that our doubly hierarchical sequential testing architecture delivers a significant throughput improvement of 2 to 6 times over the fixed sample size test while maintaining equivalent operating characteristics as measured by the Probabilities of Detection and False Alarm. We also demonstrate the throughput gains for a case study of sensing ATSC television signals in IEEE 802.22 systems. / text

The radio spectrum is becoming increasingly congested everyday with emerging technologies and with the increasing number of wireless devices. Considering the limited bandwidth availability, accommodating the demand for higher capacity and data rates is a challenging task, requiring innovative technologies that can offer new ways of exploiting the available radio spectrum. Cognitive radio arises to be a tempting solution to the spectral crowding problem by introducing the notion of opportunistic spectrum usage. Because of its attractive features, orthogonal frequency division multiplexing (OFDM) has been successfully used in numerous wireless standards and technologies. We believe that OFDM will play an important role in realizing the cognitive radio concept as well by providing a proven, scalable, and adaptive technology for air interface. The goal of this dissertation is to identify and address some of the challenges that arise from the introduction of cognitive radio. Specifically, we propose methods for obtaining awareness about channel, spectrum, and waveform in OFDM-based cognitive radio systems in this dissertation. Parameter estimation for enabling adaptation, spectrum sensing, and OFDM system identification are the three main topics discussed. OFDM technique is investigated as a candidate for cognitive radio systems. Cognitive radio features and requirements are discussed in detail, and OFDM's ability to satisfy these requirements is explained. In addition, we identify the challenges that arise from employing OFDM technology in cognitive radio. Algorithms for estimating various channel related parameters are presented. These parameters are vital for enabling adaptive system design, which is a key requirement for cognitive radio. We develop methods for estimating root-mean-square (RMS) delay spread, Doppler spread, and noise variance. The spectrum opportunity and spectrum sensing concepts are re-evaluated by considering different dimensions of the spectrum which is known as multi-dimensional spectrum space. Spectrum sensing problem in a multi-dimensional space is addressed by developing a new sensing algorithm termed as partial match filtering (PMF). Cognitive radios are expected to recognize different wireless networks and have capability of communicating with them. Algorithms for identification of multi-carrier transmissions are developed. Within the same work, methods for blindly detecting transmission parameters of an OFDM based system are developed. Blind detection is also very helpful in reducing system signaling overhead in the case of adaptive transmission where transmission parameters are changed depending on the environmental characteristics or spectrum availability.

Opportunistic spectrum reuse is a promising solution to the two main causes of spectrum scarcity: most of the radio frequency (RF) bands are allocated by static licensing, and many of them are underutilized. Frequency spectrum can be more efficiently utilized by allowing communication systems to find out unoccupied spectrum and to use it harmlessly to the licensed users. Reliable sensing of these spectral opportunities is perhaps the most essential element of this technology. Despite significant work on spectrum sensing, further performance improvement is needed to approach its full potential. In this dissertation, wireless spectrum sensing networks (WSSNs) are investigated for reliable detection of the primary (licensed) users, that enables efficient spectrum utilization and minimal power consumption in communications. Reliable spectrum sensing is studied in depth in two parts: a single sensor algorithm and then cooperative sensing are proposed based on a spectral covariance sensing (SCS). The first novel contribution uses different statistical correlations of the received signal and noise in the frequency domain. This detector is analyzed theoretically and verified through realistic simulations using actual digital television signals captured in the US. The proposed SCS detector achieves significant improvement over the existing solutions in terms of sensitivity and also robustness to noise uncertainty. Second, SCS is extended to a distributed WSSN architecture to allow cooperation between 2 or more sensors. Theoretical limits of cooperative white space sensing under correlated shadowing are investigated. We analyze the probability of a false alarm when each node in the WSSN detects the white space using the SCS detection and the base station combines individual results to make the final decision. The detection performance compared with that of the cooperative energy detector is improved and fewer sensor nodes are needed to achieve the same sensitivity. Third, we propose a low power source coding and modulation scheme for power efficient communication between the sensor nodes in WSSN. Complete analysis shows that the proposed scheme not only minimizes total power consumption in the network but also improves bit error rate (BER). / text

Cognitive radio technology is a promising technology to solve the wireless spectrum scarcity problem by intelligently allowing secondary, or unlicensed, users access to the primary, licensed, users' frequency bands. Cognitive technology involves two main tasks: 1) sensing the wireless medium to assess the presence of the primary users and 2) designing secondary spectrum access techniques that maximize the secondary users' benefits while maintaining the primary users' privileged status. On the spectrum sensing side, we make two contributions. First, we maximize a utility function representing the secondary throughput while constraining the collision probability with the primary below a certain value. We optimize therein the channel sensing time, the sensing decision threshold, the channel probing time, together with the channel sensing order for wideband primary channels. Second, we design a cooperative spectrum sensing technique termed sensing with equal gain combining whereby cognitive radios simultaneously transmit their sensing results to the fusion center over multipath fading reporting channels. The proposed scheme is shown to outperform orthogonal reporting systems in terms of achievable secondary throughput and to be robust against phase and synchronization errors. On the spectrum access side, we make four contributions. First, we design a secondary scheduling scheme with the goal of minimizing the secondary queueing delay under constraints on the average secondary transmit power and the maximum tolerable primary outage probability. Second, we design another secondary scheduling scheme based on the spectrum sensing results and the primary automatic repeat request feedback. The optimal medium access probabilities are obtained via maximizing the secondary throughput subject to constraints that guarantee quality of service parameters for the primary. Third, we propose a three-message superposition coding scheme to maximize the secondary throughput without degrading the primary rate. Cognitive relaying is employed as an incentive for the primary network. The scheme is shown to outperform a number of reference schemes such as best relay selection. Finally, we consider a network of multiple primary and secondary users. We propose a three-stage distributed matching algorithm to pair the network users. The algorithm is shown to perform close to an optimal central controller, albeit at a reduced computational complexity.

Όπως δηλώνει και ο τίτλος, η παρούσα διπλωματική εργασία διαπραγματεύεται διάφορες τεχνικές για την ανίχνευση του φάσματος σε cognitive radio SIMO συστήματα. Η συμβατική προσέγγιση της διαχείρισης του φάσματος δεν είναι ευέλικτη καθώς με το περισσότερο χρήσιμο τμήμα του ραδιοφάσματος να είναι δεσμευμένο, είναι εξαιρετικά δύσκολο να βρεθούν ελεύθερες συχνότητες για την ανάπτυξη νέων υπηρεσιών ή για τον εμπλουτισμό των ήδη υπαρχόντων, ενώ ταυτόχρονα, διάφορες μετρήσεις έχουν καταδείξει ότι το αδειοδοτημένο φάσμα σπάνια χρησιμοποιείται πλήρως, τόσο ως προς το πεδίο του χρόνου όσο και ως προς το πεδίο του χώρου. Έτσι, η τεχνολογία του Cognitive Radio (Γνωστικά Συστήματα Ραδιοεπικοινωνιών) έρχεται να προσφέρει λύση, κυρίως, στα παραπάνω ζητήματα παρέχοντας δυναμική εκμετάλλευση του φάσματος. Η τεχνολογία του Cognitive Radio έχει προταθεί για μικρότερης προτεραιότητας δευτερεύοντα συστήματα αποσκοπώντας στη βελτίωση της αποδοτικότητας του διαθέσιμου φάσματος μέσω της ανίχνευσής του και επιτρέποντας στα δευτερεύοντα αυτά συστήματα να εκπέμπουν στις μπάντες που εντοπίζονται να μη χρησιμοποιούνται. Όπως γίνεται εύκολα αντιληπτό από τα παραπάνω, η ανίχνευση φάσματος (spectrum sensing) αποτελεί ένα ιδιαιτέρως κρίσιμο θέμα για τα cognitive συστήματα. Για να επιτευχθεί η προσαρμοστική μετάδοση σε αχρησιμοποίητα τμήματα φάσματος, χωρίς να προκαλούνται παρεμβολές στους βασικούς χρήστες αυτών των τμημάτων (Primary Users-PUs), το spectrum sensing αποτελεί το πρώτο και ένα από τα κυριότερα βήματα, καθώς απαιτείται υψηλή αξιοπιστία στην ανίχνευση του σήματος των PUs. Οι δευτερεύοντες χρήστες (Secondary Users-SUs), δηλαδή, θα πρέπει να γνωρίζουν αν το φάσμα χρησιμοποιείται ώστε να αξιοποιήσουν το διαθέσιμο φάσμα με τον πιο αποτελεσματικό τρόπο. Ουσιαστικά, το spectrum sensing εφαρμόζεται για να δώσει στον cognitive χρήστη μια όσο το δυνατόν πιστότερη εικόνα του περιβάλλοντος στο οποίο βρίσκεται. Σκοπό της παρούσας διπλωματικής εργασίας αποτελεί η μελέτη και η ανάπτυξη αλγορίθμων που θα επιτρέπουν στον SU ενός SIMO συστήματος να ανιχνεύει την ύπαρξη φασματικών κενών. Η υλοποίηση που χρησιμοποιήσαμε βασίζεται στη χρήση ενός predictor. Πιο συγκεκριμένα, το σήμα που λαμβάνει ο δέκτης περνά από ένα backward linear predictor από τον οποίο υπολογίζουμε τη διαφορά του προβλεπόμενου σήματος σε σχέση με το πραγματικό, δηλαδή το σφάλμα πρόβλεψης. Αξιοποιώντας κατάλληλα το σφάλμα πρόβλεψης, και πιο συγκεκριμένα τον πίνακα αυτοσυσχέτισης του σφάλματος, μας δίνεται η δυνατότητα να ανιχνεύσουμε αξιόπιστα την ύπαρξη ή την απουσία σήματος, ακόμα και σε θορυβώδη περιβάλλοντα, δηλαδή για χαμηλές τιμές του λόγου σήματος προς θόρυβο. Για τον έλεγχο της απόδοσης των αλγορίθμων που αναπτύξαμε, το παραπάνω σύστημα εξομοιώθηκε σε MATLAB για διάφορες συνθήκες και κανάλια / In the present thesis, we will study spectrum sensing techniques of Cognitive Radio SIMO systems. The conventional approach to spectrum management is not flexible, as most of the useful part of the spectrum is bounded. Hence it is extremely difficult to find free frequencies in order to deploy new services or to enhance the already existing ones. At the same time, various measurements show that the licensed spectrum is heavily underutilized in terms of both the time domain as well as the space domain. Thus Cognitive Radio technology comes to offer solutions, mainly with regard to the issues mentioned above, providing a dynamic utilization of the spectrum. Cognitive Radio has been proposed for lower priority secondary systems intending to improve spectral efficiency through spectrum sensing thus allowing these systems to transmit at frequency bands that are detected to be unused. As we can easily understand from the above, spectrum sensing is a critical issue for cognitive systems. In order to achieve adaptive transmission in unused portions of the spectrum without interferences to the licensed users of these portions (Primary Users-PUs), spectrum sensing is the first and one of the most important steps as high reliability is demanded on PUs' signal detection. That is, Secondary Users (SUs) should know if the spectrum is being used in order to exploit the available spectrum in the most efficient way. Essentially, spectrum sensing is used in order to provide the cognitive user with a representation of its operating environment which is as faithful as possible. The scope of this thesis is the study and the creation of algorithms that will give the SU of a SIMO system the opportunity to detect the existence of spectrum holes. The implementation we used is based on a predictor. More specifically, the received signal passes through a backward linear predictor from which we compute the difference between the actual signal and the predicted signal, which is the prediction error. By properly exploiting the prediction error, more precisely the autocorrelation matrix of the prediction error, we can trustworthily detect the existence or the absence of a signal, even in noisy environments, that is, for low values of the signal-to-noise ratio. In order to test the performance of our algorithms, the system above was simulated by MATLAB for different conditions and channels.

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