Η παρούσα διπλωματική εργασία έχει ως αντικείμενο την μελέτη και ανάπτυξη μίας τεχνικής ανίχνευσης φάσματος (spectrum sensing technique), η οποία να μπορεί να χρησιμοποιηθεί σε περιβάλλον Δυναμικής Εκχώρησης Φάσματος από Γνωστικά Συστήματα Ραδιοεπικοινωνιών (Cognitive Radio Systems). Οι παραδοσιακές στατικές στρατηγικές καταμερισμού του φάσματος έχουν δημιουργήσει προβλήματα έλλειψης διαθέσιμου φάσματος. Ταυτόχρονα, πρόσφατες μετρήσεις δείχνουν ότι μεγάλα τμήματα του φάσματος που έχουν εκχωρηθεί με άδεια σε συγκεκριμένα συστήματα υποχρησιμοποιούνται. Είναι επομένως αναγκαίο να υιοθετηθούν νέες πολιτικές διαχείρισης του φάσματος οι οποίες θα επιτρέπουν σε μη αδειοδοτημένα δίκτυα να κάνουν χρήση τμημάτων του αδειοδοτημένου φάσματος. Τα Γνωστικά Συστήματα Ραδιοεπικοινωνιών είναι ευφυή συστήματα τα οποία έχουν γνώση του περιβάλλοντός τους και μπορούν να προσαρμόζουν κατάλληλα τις παραμέτρους λειτουργίας τους σε αυτό. Τα συστήματα αυτά μπορούν να ανιχνεύουν περιοδικά το φάσμα, να εντοπίζουν τις ζώνες συχνοτήτων οι οποίες δε χρησιμοποιούνται από τους αδειοδοτημένους χρήστες τους και να τις αξιοποιούν. Όπως γίνεται εύκολα αντιληπτό από τα παραπάνω η ανίχνευση φάσματος αποτελεί ένα ιδιαιτέρως κρίσιμο θέμα για τα Γνωστικά Συστήματα Ραδιοεπικοινωνιών. Στο στάδιο αυτό, το σύστημα ανιχνεύει και παρακολουθεί στο περιβάλλον μέσα στο οποίο ενεργεί, το κατά πόσο το φάσμα είναι ελεύθερο ανά πάσα χρονική στιγμή και αξιοποιεί αυτά τα φασματικά κενά. Ουσιαστικά η ανίχνευση φάσματος εφαρμόζεται για να δώσει στον cognitive χρήστη μία όσο το δυνατόν πιστότερη εικόνα του περιβάλλοντος μέσα στο οποίο βρίσκεται. Η δική μας μελέτη επικεντρώθηκε στις τεχνικές ανίχνευσης φάσματος (spectrum sensing) και συγκεκριμένα αναπτύσσουμε μία μέθοδο ανίχνευσης φασματικών κενών βασιζόμενη στη χρήση ενός προβλεπτή (predictor) και στη χρησιμοποίηση του σφάλματος πρόβλεψης του σήματος που προκύπτει από αυτόν ως μετρική για τη λήψη απόφασης σχετικά με την ύπαρξη ή την απουσία σήματος ακόμα και σε θορυβώδη περιβάλλοντα (πολύ χαμηλό SNR). H τεχνική ανίχνευσης φάσματος που προτείνουμε μοντελοποιήθηκε στο περιβάλλον μοντελοποίησης MATLAB. Στη συνέχεια, διενεργήθηκαν εκτενείς προσομοιώσεις για ποικίλες τιμές των διαφόρων παραμέτρων του συστήματος αλλά και για διαφορετικά συστήματα, ούτως ώστε να αξιολογηθεί η επίδοση της τεχνικής σε διάφορες συνθήκες. / 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 power 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.

There has been a recent explosive growth in mobile data consumption. This, in turn, imposes many challenges for mobile services providers and regulators in many aspects. One of these primary challenges is maintaining the radio spectrum to handle the current and upcoming expansion in mobile data traffic. In this regard, a radio spectrum regulatory framework based on secondary spectrum access is proposed as one of the solutions for the next generation wireless networks. In secondary spectrum access framework, secondary (unlicensed) systems coexist with primary (licensed) systems and access the spectrum on an opportunistic base. In this thesis, aspects related to finding the free of use spectrum portions - called spectrum opportunities - are treated. One way to find these opportunities is spectrum sensing which is considered as an enabler of opportunistic spectrum access. In particular, this thesis investigates some topics in blind spectrum sensing where no priori knowledge about the possible co-existing systems is available. As a standalone contribution in blind spectrum sensing arena, a new blind sensing technique is developed in this thesis. The technique is based on discriminant analysis statistical framework and called spectrum discriminator (SD). A comparative study between the SD and some existing blind sensing techniques was carried out and showed a reliable performance of the SD. The thesis also contributes by exploring sensing parameters optimization for two existing techniques, namely, energy detector (ED) and maximum-minimum eigenvalue detector (MME). For ED, the sensing time and periodic sensing interval are optimized to achieve as high detection accuracy as possible. Moreover, a study of sensing parameters optimization in a real-life coexisting scenario, that is, LTE cognitive femto-cells, is carried out with an objective of maximizing cognitive femto-cells throughput. In association with this work, an empirical statistical model for LTE channel occupancy is accomplished. The empirical model fits the channels' active and idle periods distributions to a linear combination of multiple exponential distributions. For the MME, a novel solution for the filtering problem is introduced. This solution is based on frequency domain rectangular filtering. Furthermore, an optimization of the observation bandwidth for MME with respect to the signal bandwidth is analytically performed and verified by simulations. After optimizing the parameters for both ED and MME, a two-stage fully-blind self-adapted sensing algorithm composed of ED and MME is introduced. The combined detector is found to outperform both detectors individually in terms of detection accuracy with an average complexity lies in between the complexities of the two detectors. The combined detector is tested with measured TV and wireless microphone signals. The performance evaluation in the different parts of the thesis is done through measurements and/or simulations. Active measurements were performed for sensing performance evaluation. Passive measurements on the other hand were used for LTE downlink channels occupancy modeling and to capture TV and wireless microphone signals. / <p>QC 20150209</p>

Με τη ραγδαία ανάπτυξη των ασύρματων επικοινωνιών και την μαζική χρήση τους, εμφανίστηκε το πρόβλημα της διάθεσης των ραδιοσυχνοτήτων του φάσματος, του κύριου αλλά πεπερασμένου πόρου για τις ασύρματες επικοινωνίες. Η κύρια πολιτική πρόσβασης στο φάσμα ραδιοσυχνοτήτων, είναι η εξουσιοδότηση επιλεγμένων χρηστών να μεταδίδουν σε συγκεκριμένο εύρος συχνοτήτων. Παρά την κάλυψη όλων των ραδιοσυχνοτήτων από εξουσιοδοτημένους χρήστες, την αυξημένη ζήτηση και το υψηλό κόστος πρόσβασης, μετά από έρευνες αποδεδείχθηκε ότι μόνο το 70% του φάσματος χρησιμοποιείται αποδοτικά μέχρι σήμερα. Η τεχνολογία του Cognitive Radio αναπτύχθηκε με την προοπτική να επιτύχει αποτελεσματικότερη χρήση του φάσματος, δίνοντας τη δυνατότητα σε μη εξουσιοδοτημένους χρήστες να έχουν πρόσβαση σε συχνότητες που είναι καθόλου ή μερικώς κατειλημμένες από τους εξουσιοδοτημένους χρήστες, στο χώρο και στο χρόνο. Η τεχνολογία του Cognitive Radio εφαρμόζει δυο βήματα. Πρώτα αντιλαμβάνεται την κατάσταση του φάσματος στο χώρο σε συγκεκριμένες χρονικές στιγμές και έπειτα διαθέτει δυναμικά τις ελεύθερες συχνότητες που εντόπισε στους μη εξουσιοδοτημένους χρήστες, η διαδικασίες ονομάζονται ανίχνευση και κατανομή φάσματος αντίστοιχα. Ο μόνος περιορισμός είναι, το εκπεμπόμενο σήμα των μη εξουσιοδοτημένων χρηστών να μην παρεμβαίνει (με τη μορφή θορύβου) στο σήμα των εξουσιοδοτημένων χρηστών. Σε αυτήν την εργασία θα υλοποιηθεί μια μέθοδος της διαδικασίας ανίχνευσης φάσματος και οι τεχνικές που την εφαρμόζουν. Ο κύριος στόχος της ανίχνευσης φάσματος είναι ο εντοπισμός των εξουσιοδοτημένων χρηστών όταν αυτοί εκπέμπουν στις καθορισμένες για τον καθένα συχνότητες. Αυτό επιτυγχάνεται όταν φτάνει το σήμα των εξουσιοδοτημένων χρηστών στην κεραία του μη εξουσιοδοτημένου χρήστη. To βασικό εμπόδιο που παρουσιάζεται για τον εντοπισμό αυτών είναι η εξασθένηση του σήματος του εξουσιοδοτημένου χρήστη εξαιτίας των κακών συνθηκών καναλιού που προκύπτουν από τα φαινόμενα multipath, distance dependent path loss και shadowing. Μελέτες έδειξαν ότι η συνεργασία των μη εξουσιοδοτημένων χρηστών σε ένα δίκτυο μπορεί να ακυρώσει την επίδραση τέτοιων φαινόμενων στη διαδικασία εντοπισμού. Έτσι έχουν αναπτυχθεί ποικίλες τεχνικές ανίχνευσης φάσματος βασισμένες στη συνεργασία των μη εξουσιοδοτημένων χρηστών. Η παρούσα εργασία υλοποιεί μια μέθοδο συνεργαζόμενης ανίχνευσης φάσματος που βασίζεται στην ενέργεια του σήματος. Λόγω του κινδύνου αλλοίωσης αποτελεσμάτων από την παρουσία κακόβουλων χρηστών σε συστήματα συνεργασίας, η τεχνική συνεργασίας που επιλέχθηκε εστιάζει στην προστασία του δικτύου από κακόβουλους χρήστες. Μια τέτοια τεχνική θα συγκέντρωνε όλη την απαιτούμενη επεξεργαστική ισχύ σε έναν μη εξουσιοδοτημένο χρήστη που θα αποτελούσε το κέντρο παραγωγής των αποφάσεων-το fusion center. Στην εργασία αυτή η απαιτούμενη επεξεργαστική ισχύς κατανέμεται σε όλους τους μη εξουσιοδοτημένους χρήστες. Αυτό επιτυγχάνεται εισάγοντας ένα επιπλέον βήμα στη διαδικασία. Οι μη εξουσιοδοτημένοι χρήστες εκτελούν αρχικά μια νέα τεχνική ανίχνευσης φάσματος μεμονωμένα, ώστε η τελική απόφαση του fusion center να αφορά αυτές τις συχνότητες για τις οποίες δεν υπήρξε ταύτιση από την πλειοψηφία τους. Η νέα τεχνική που θα εκτελείται μεμονωμένα από τους μη εξουσιοδοτημένους χρήστες είναι μια τεχνική ανίχνευσης φάσματος που δεν διακρίνεται για τα καλά της αποτελέσματα και η μόνη εγγύηση που μπορεί να προσφέρει είναι ο ακριβής εντοπισμός των συχνοτήτων στις οποίες οι εξουσιοδοτημένοι χρήστες δεν μεταδίδουν, θυσιάζοντας πιθανώς κατειλημμένες συχνότητες. Η στοιχειώδης λειτουργία αυτής της τεχνικής σε συνδυασμό με τις ανύπαρκτες απαιτήσεις σε δεδομένα εκ των προτέρων γνωστά, την χαρακτηρίζει πλήρως κατάλληλη για πρώτο βήμα στη μέθοδο που αναπτύχθηκε. / Due to rapid growth of wireless communications and the massive use of them, the problem of sharing the radio spectrum, the main though finite source of wireless communication, made its appearance. The main radio spectrum access policy is to predefine users -named primary- for transmitting to particular radio frequencies. Nevertheless the authorization of the whole the radio spectrum, given the strong competition and the high financial cost for access, doesn’t exploit completely the source. On the contrary, researches have shown that only the 70 % of the radio spectrum is effectively used. The Cognitive Radio technology was developed with the prospect to achieve a more effective use of spectrum, by giving the chance of transmission to non authorization users -secondary- in frequencies which are partially or completely unoccupied with primary users’ signals, from the perspectives of time and space. Cognitive Radio technology applies two processes. At first it senses the spectrum current flow in particular space and time periods, then it dynamically sharing those available frequencies which it sensed, to secondary users. These processes named as Spectrum Sensing and Spectrum Access respectively. The only restriction define to that, transmitted signal of secondary users is forbidden from interfering with primary user signal. In this study, a method of Spectrum Sensing process and individual techniques will be developed. The main objective of Spectrum Sensing process is to determine primary users when they transmit to predefined frequencies. This can be accomplished provided that the signal of primary user can be received from secondary user. Signal deterioration due to channel conditions could be a reason for secondary users in order to not receive primary user signal. Some of these conditions are multipath, distance dependent path loss και shadowing phenomenon. Researches have shown that the secondary users’ cooperation can avoid the effect of those conditions in spectrum sensing process. Thus a variety of spectrum sensing techniques have been developed, which are based on secondary users’ cooperation. In the present study is performed an energy based cooperative spectrum sensing method. Due to the possibility of cooperating with malicious users in the process, the performed cooperation technique focuses on protection from malicious users. Note that such a technique will concentrate the whole computing power on a single secondary user, which one make the final decision and named fusion center. The method of this study distributes the computing power among all the secondary users. That happens by adding one more step in the process. Secondary users firstly execute a spectrum sensing technique individually, in order the process of fusion center to affect only those frequencies, which secondary individual decisions achieved a degree of unanimity for. The individual technique executed by secondary users is not typical of good results in sensing the primary users who transmit, however it gives a guarantee of small values in false alarm possibility. The fundamental operation of this technique in coexistence with very few a-priory requirements made it the appropriate technique for the first step of our method.

The idea that the radio spectrum is growing ever more scarce has become commonplace, and is being reinforced by the recent bidding wars among telecom operators. New wireless applications tend to be deployed in the relatively narrow unlicensed frequency bands, worsening the problem of interference for all users.  However, not all frequency bands are in use in every location all the time, creating temporal and spatial gaps (also known as white spaces) that cognitive radio systems aim to take advantage of. In order to achieve that, such systems need to be able to constantly scan large chunks of the radio spectrum to keep track of which frequency bands are locally available any given moment, thus allowing users to switch to one of these unoccupied frequency bands once the current band becomes unusable (or less useful). This requirement of wideband sensing capabilities often translates into the need to install specialized radio components, raising the costs of such systems, and is often at odds with the focus on monitoring the current band as is done by traditional wireless devices. The goal of this master’s thesis project is to simplify cognitive radio systems by shifting the wideband sensing functionality to a specialized and inexpensive embedded platforms that will act as a white space sensor, thus freeing cognitive radio users from this task and making it easier to integrate dynamic spectrum management techniques into existing systems. To do that a wireless sensor gateway platform developed by a previous master’s thesis has been repurposed as a prototype white space detector and tested against several wireless transmitters.  The aim is to develop a standalone platform that can be deployed all around an area to collect data that can be used to create a geographical map of the use of the spectrum. Such a system should require as little maintenance as possible, thus auto-update and self-configuring features have been implemented in the detector, as well as a simple scanning protocol that allows for remote configuration of the wideband sensing parameters. Furthermore, a basic server has been developed to aggregate and display the data provided by the different sensors. / Tanken att radiospektrum blir allt knappare har blivit vardagsmat, och förstärks av de senaste budgivning krig mellan teleoperatörer. Nya trådlösa applikationer tenderar att sättas i de relativt smala olicensierade frekvensband, förvärrade problemet med störningar för alla användare. Men inte alla frekvensband som används i varje plats hela tiden, skapar tidsmässiga och rumsliga luckor (även känd som vita fläckar) som kognitiva radiosystem syftar till att dra nytta av.  För att uppnå detta, sådana system måste hela tiden kunna scanna stora delar av radiospektrum för att hålla reda på vilka frekvensband är lokalt tillgängliga varje givet ögonblick, vilket gör omkopplaren när den nuvarande bandet blir obrukbar.  Det här kravet på bredbands avkänning kapaciteter översätter ofta in behovet av att installera specialiserade radiokomponenter, höja kostnaderna för sådana system, och är ofta i strid med fokus på övervakning av strömmen band med traditionella trådlösa enheter. Målet med detta examensarbete är att förenkla kognitiva radiosystem med wideband avkänning funktionalitet till en specialiserad och billig inbäddad plattform som kommer att fungera som ett vitt utrymme sensor, vilket frigör kognitiva radio användare från denna uppgift och gör det enklare att integrera dynamiskt spektrum förvaltning tekniker i befintliga system. För att göra det en trådlös sensor gateway plattform som utvecklats av ett tidigare examensarbete har apterat som en prototyp blanktecken detektor och testas mot flera trådlösa sändare. Målet är att utveckla en fristående plattform som kan sättas runt för att skapa en geografisk karta av användningen av spektrum och kräva så lite underhåll som möjligt, har automatisk uppdatering och självkonfigurerande funktioner implementerats i detektorn, samt som en enkel scanning protokoll som möjliggör fjärrkonfiguration av den bredbandiga avkänningsparametrarna. Dessutom har en grundläggande server utvecklats för att aggregera och visa uppgifterna från de olika sensorerna.

In 2015, Federal Communications Commission (FCC) enabled shared commercial use of the 3.550-3.700 GHz band. A framework was developed to enable this spectrum-sharing capa- bility which included an automated frequency coordinator called Spectrum Access System (SAS). This work extends the open source SAS based on the aforementioned FCC SAS framework developed by researchers at Virginia Tech Wireless group, with real-time envi- ronment sensing capability along with intelligent incumbent detection using Software-defined Radios (SDRs) and a real-time graphical user interface. This extended version is called the OpenSAS. Furthermore, the SAS client and OpenSAS are extended to be compliant with the Wireless Innovation Forum (WINNF) specifications by testing the SAS-CBRS Base Station Device (CBSD) interface with the Google SAS Test Environment. The Environment Sensing Capability (ESC) functionality is evaluated and tested in our xG Testbed to verify its ability to detect the presence of users in the CBRS band. An ML-based feedforward neural net- work model is employed and trained using simulated radar waveforms as incumbent signals and captured 5G New Radio (NR) signals as a non-incumbent signal to predict whether the detected user is a radar incumbent or an unknown user. If the presence of incumbent radar is detected with an 85% or above certainty, incumbent protection is activated, terminating CBSD grants causing damaging interference to the detected incumbent. A 5G NR signal is used as a non-incumbent user and added to the training dataset to better the ability of the model to reject non-incumbent signals. The model achieves a maximum validation accuracy of 95.83% for signals in the 40-50 dB Signal-to-Noise Ratio (SNR) range. It achieves an 85.35% accuracy for Over the air (OTA) real-time tests. The non-incumbent 5G NR signal rejection accuracy is 91.30% for a calculated SNR range of 10-20 dB. In conclusion, this work advances state of the art in spectrum sharing systems by presenting an enhanced open source SAS and evaluating the newly added functionalities. / Master of Science / In 2015, Federal Communications Commission (FCC) enabled shared commercial use of the 3.550-3.700 GHz band. A framework was developed to enable this spectrum-sharing capability which included an automated frequency coordinator called Spectrum Access System (SAS). The task of the SAS is to make sure no two users use the same spectrum in the same location causing damaging interference to each other. The SAS is also responsible for prioritizing the higher tier users and protecting them from interference from lower tier users. This work extends the open source SAS based on the aforementioned FCC SAS framework developed by researchers at Virginia Tech Wireless group, with real-time environment sensing capability along with intelligent incumbent detection using Software-defined Radios (SDRs) and a real-time graphical user interface. This extended version is called the OpenSAS. Furthermore, the SAS client and OpenSAS are extended to be compliant with the Wireless Innovation Forum (WINNF) specifications by testing the SAS-CBRS Base Station Device (CBSD) interface with the Google SAS Test Environment. The Environment Sensing Capability (ESC) functionality is evaluated and tested in our xG Testbed to verify its ability to detect the presence of users in the CBRS band. The ESC is used to detect incumbent users (the highest tier) that do not inform the SAS about their use of the spectrum. An ML-based feedforward neural net- work model is employed and trained using simulated radar waveforms as incumbent signals and captured 5G New Radio (NR) signals as a non-incumbent signal to predict whether the detected user is a radar incumbent or an unknown user. If the presence of incumbent radar is detected with an 85% or above certainty, incumbent protection is activated, terminating CBSD grants causing damaging interference to the detected incumbent. A 5G NR signal is used as a non-incumbent user and added to the training dataset to better the ability of the model to reject non-incumbent signals. The model achieves a maximum validation accuracy of 95.83% for signals in the 40-50 dB Signal to-Noise Ratio (SNR) range. It achieves an 85.35% accuracy for Over the air (OTA) real-time tests. The non-incumbent 5G NR signal rejection accuracy is 91.30% for a calculated SNR range of 10-20 dB. In conclusion, this work advances state of the art in spectrum sharing systems by presenting an enhanced open source SAS and evaluating the newly added functionalities.

Spectrum sensing in wireless communications serves as a crucial binary classification tool in cognitive radios, facilitating the detection of available radio spectrums for secondary users, especially in scenarios with high Signal-to-Noise Ratio (SNR). Leveraging Liquid State Machines (LSMs), which emulate spiking neural networks like the ones in the human brain, prove to be highly effective for real-time data monitoring for such temporal tasks. The inherent advantages of LSM-based recurrent neural networks, such as low complexity, high power efficiency, and accuracy, surpass those of traditional deep learning and conventional spectrum sensing methods. The architecture of the liquid state machine processor and its training methods are crucial for the performance of an LSM accelerator. This thesis presents one such LSM-based accelerator that explores novel architectural improvements for LSM hardware. Through the adoption of triplet-based Spike-Timing-Dependent Plasticity (STDP) and various spike encoding schemes on the spectrum dataset within the LSM, we investigate the advantages offered by these proposed techniques compared to traditional LSM models on the FPGA. FPGA boards, known for their power efficiency and low latency, are well-suited for time-critical machine learning applications. The thesis explores these novel onboard learning methods, shares the results of the suggested architectural changes, explains the trade-offs involved, and explores how the improved LSM model's accuracy can benefit different classification tasks. Additionally, we outline the future research directions aimed at further enhancing the accuracy of these models. / Master of Science / Machine Learning (ML) and Artificial Intelligence (AI) have significantly shaped various applications in recent years. One notable domain experiencing substantial positive impact is spectrum sensing within wireless communications, particularly in cognitive radios. In light of spectrum scarcity and the underutilization of RF spectrums, accurately classifying spectrums as occupied or unoccupied becomes crucial for enabling secondary users to efficiently utilize available resources. Liquid State Machines (LSMs), made of spiking neural networks resembling human brain, prove effective in real-time data monitoring for this classification task. Exploiting the temporal operations, LSM accelerators and processors, facilitate high performance and accurate spectrum monitoring than conventional spectrum sensing methods. The architecture of the liquid state machine processor's training and optimal learning methods plays a pivotal role in the performance of a LSM accelerator. This thesis delves into various architectural enhancements aimed at spectrum classification using a liquid state machine accelerator, particularly implemented on an FPGA board. FPGA boards, known for their power efficiency and low latency, are well-suited for time-critical machine learning applications. The thesis explores onboard learning methods, such as employing a targeted encoder and incorporating Triplet Spike Timing-Dependent Plasticity (Triplet STDP) in the learning reservoir. These enhancements propose improvements in accuracy for conventional LSM models. The discussion concludes by presenting results of the architectural implementations, highlighting trade-offs, and shedding light on avenues for enhancing the accuracy of conventional liquid state machine-based models further.

Dynamic spectrum access (DSA) wireless networks focus on using RF spectrum more efficiently and dynamically. Significant progress has been made during the past few years. For example, many measurements of current spectrum utilization are available. Theoretical analyses and computational simulations of DSA networks also abound. In sharp contrast, few network systems, particularly those with a decentralized structure, have been built even at a small scale to investigate the performance, behavior, and dynamics of DSA networks under different scenarios. This dissertation provides the theory, design, and implementation of a software radio-based decentralized DSA network prototype, and its enabling technologies: software radio, signal detection and classification, and distributed cooperative spectrum sensing. By moving physical layer functions into the software domain, software radio offers an unprecedented level of flexibility in radio development and operation, which can facilitate research and development of cognitive radio (CR) and DSA networks. However, state-of-the-art software radio systems still have serious performance limitations. Therefore, a performance study of software radio is needed before applying it in any development. This dissertation investigates three practical issues governing software radio performance that are critical in DSA network development: RF front end nonlinearity, dynamic computing resource allocation, and execution latency. It provides detailed explanations and quantitative results on SDR performance. Signal detection is the most popular method used in DSA networks to guarantee non-interference to primary users. Quickly and accurately detecting signals under all possible conditions is challenging. The cyclostationary feature detection method is attractive for detecting primary users because of its ability to distinguish between modulated signals, interference, and noise at a low signal-to-noise ratio (SNR). However, a key issue of cyclostationary signal analysis is the high computational cost. To tackle this challenge, parallel computing is applied to develop a cyclostationary feature based signal detection method. This dissertation presents the method's performance on multiple signal types in noisy and multi-path fading environments. Distributed cooperative spectrum sensing is widely endorsed to monitor the radio environment so as to guarantee non-interference to incumbent users even at a low SNR and under hostile conditions like shadowing, fading, interference, and multi-path. However, such networks impose strict performance requirements on data latency and reliability. Delayed or faulty data may cause secondary users to interfere with incumbent users because secondary users could not be informed quickly or reliably. To support such network performance, this dissertation presents a set of data process and management schemes in both sensors and data fusion nodes. Further, a distributed cooperative sensor network is built from multiple sensors; together, the network compiles a coherent semantic radio environment map for DSA networks to exploit available frequencies opportunistically. Finally, this dissertation presents the complete design of a decentralized and asynchronous DSA network across the PHY layer, MAC layer, network layer, and application layer. A ten-node prototype is built based on software radio technologies, signal detection and classification methods, distributed cooperative spectrum sensing systems, dynamic wireless protocols, and a multi-channel allocation algorithm. Systematic experiments are carried out to identify several performance determining factors for decentralized DSA networks. / Ph. D.

The rise of 5G and beyond systems has fuelled research in merging machine learning with wireless communications to achieve cognitive radios. However, the portability and limited power supply of radio frequency devices limits engineers' ability to combine them with powerful predictive models. This hinders the ability to support advanced 5G applications such as device-to-device (D2D) communication and dynamic spectrum sharing (DSS). This challenge has inspired a wave of research in energy efficient machine learning hardware with low computational and area overhead. In particular, hardware implementations of the delayed feedback reservoir (DFR) model show promising results for meeting these constraints while achieving high accuracy in cognitive radio applications. This thesis answers two research questions surrounding the applicability of FPGA DFR systems for DSS. First, can a DFR network implemented on an FPGA run faster and with lower power than a purely software approach? Second, can the system be implemented efficiently on an edge device running at less than 10 watts? Two systems are proposed that prove FPGA DFRs can achieve these feats: a mixed-signal circuit, followed by a high-level synthesis circuit. The implementations execute up to 58 times faster, and operate at more than 90% lower power than the software models. Furthermore, the lowest recorded average power of 0.130 watts proves that these approaches meet typical edge device constraints. When validated on the NARMA10 benchmark, the systems achieve a normalized error of 0.21 compared to state-of-the-art error values of 0.15. In a DSS task, the systems are able to predict spectrum occupancy with up to 0.87 AUC in high noise, multiple input, multiple output (MIMO) antenna configurations compared to 0.99 AUC in other works. At the end of this thesis, the trade-offs between the approaches are analyzed, and future directions for advancing this study are proposed. / Master of Science / The rise of 5G and beyond systems has fuelled research in merging machine learning with wireless communications to achieve cognitive radios. However, the portability and limited power supply of radio frequency devices limits engineers' ability to combine them with powerful predictive models. This hinders the ability to support advanced 5G and internet-of-things (IoT) applications. This challenge has inspired a wave of research in energy efficient machine learning hardware with low computational and area overhead. In particular, hardware implementations of a low complexity neural network model, called the delayed feedback reservoir, show promising results for meeting these constraints while achieving high accuracy in cognitive radio applications. This thesis answers two research questions surrounding the applicability of field-programmable gate array (FPGA) delayed feedback reservoir systems for wireless communication applications. First, can this network implemented on an FPGA run faster and with lower power than a purely software approach? Second, can the network be implemented efficiently on an edge device running at less than 10 watts? Two systems are proposed that prove the FPGA networks can achieve these feats. The systems demonstrate lower power consumption and latency than the software models. Additionally, the systems maintain high accuracy on traditional neural network benchmarks and wireless communications tasks. The second implementation is further demonstrated in a software-defined radio architecture. At the end of this thesis, the trade-offs between the approaches are analyzed, and future directions for advancing this study are proposed.

Traditional wireless networks are regulated by a fixed spectrum assignment policy. This results in situations where most of the allocated radio spectrum is not utilized. In order to address this spectrum underutilization, cognitive radio (CR) has emerged as a promising solution. Spectrum sensing is an essential component in CR networks to discover spectrum opportunities. The most common spectrum sensing techniques are energy detection, matched filtering or cyclostationary feature detection, which aim to maximize the probability of detection subject to a certain false alarm rate. Besides probability of detection, detection delay is also a crucial criterion in spectrum sensing. In an interweave CR network, quick detection of the absence of primary user (PU), which is the owner of the licensed spectrum, allows good utilization of unused spectrum, while quick detection of PU transmission is important to avoid any harmful interference. This thesis consider quickest spectrum sensing, where the aim is to detect the PU with minimal detection delay subject to a certain false alarm rate. In the earlier chapters of this thesis, a single antenna cognitive user (CU) is considered and we study quickest spectrum sensing performance in Gaussian channel and classical fading channel models, including Rayleigh, Rician, Nakagami-m and a long-tailed channel. We prove that the power of the complex received signal is a sufficient statistic and derive the probability density function (pdf) of the received signal amplitude for all of the fading cases. The novel derivation of the pdfs of the amplitude of the received signal for the Rayleigh, Rician and Nakagami-m channels uses an approach which avoids numerical integration. We also consider the event of a mis-matched channel, where the cumulative sum (CUSUM) detector is designed for a specific channel, but a different channel is experienced. This scenario could occur in CR network as the channel may not be known and hence the CUSUM detector may be experiencing a different channel. Simulations results illustrate that the average detection delay depends greatly on the channel but very little on the nature of the detector. Hence, the simplest time-invariant detector can be employed with minimal performance loss. Theoretical expressions for the distribution of detection delay for the time-invariant CUSUM detector, with single antenna CU are developed. These are useful for a more detailed analysis of the quickest spectrum sensing performance. We present several techniques to approximate the distribution of detection delay, including deriving a novel closed-form expression for the detection delay distribution when the received signal experiences a Gaussian channel. We also derive novel approximations for the distribution of detection delay for the general case due to the absence of a general framework. Most of the techniques are general and can be applied to any independent and identically distributed (i.i.d) channel. Results show that different signal-to-noise ratio (SNR) and detection delay conditions require different methods in order to achieve good approximations of the detection delay distributions. The remarkably simple Brownian motion approach gives the best approximation for longer detection delays. In addition, results show that the type of fading channel has very little impact on long detection delays. In later chapters of this thesis, we employ multiple receive antennas at the CU. In particular, we study the performance of multi-antenna quickest spectrum sensing when the received signal experiences Gaussian, independent and correlated Rayleigh and Rician channels. The pdfs of the received signals required to form the CUSUM detector are derived for each of the scenarios. The extension into multiple antennas allows us to gain some insight into the reduction in detection delay that multiple antennas can provide. Results show that the sensing performance increases with an increasing Rician K-factor. In addition, channel correlation has little impact on the sensing performance at high SNR, whereas at low SNR, increasing correlation between channels improves the quickest spectrum sensing performance. We also consider mis-matched channel conditions and show that the quickest spectrum sensing performance at a particular correlation coefficient or Rician K-factor depends heavily on the true channel irrespective of the number of antennas at the CU and is relatively insensitive to the channel used to design the CUSUM detector. Hence, a simple multi-antenna time-invariant detector can be employed. Based on the results obtained in the earlier chapters, we derive theoretical expressions for the detection delay distribution when multiple receive antennas are employed at the CU. In particular, the approximation of the detection delay distribution is based on the Brownian motion approach.

Esta tese de doutorado trata de algoritmos de detecção cooperativa aplicados ao problema de sensoriamento espectral em sistemas de rádios cognitivos. O problema de detecção cooperativa é abordado sob dois paradigmas distintos: detecção centralizada e distribuída. No primeiro caso, considera-se que o sistema conta com um centro de fusão responsável pela tomada de decisão no processo de detecção. Já no segundo caso, considera-se que os rádios cognitivos da rede trocam informações entre si e as decisões são tomadas localmente. No que concerne ao sensoriamento espectral centralizado, são estudados os casos em que os rádios cognitivos enviam apenas um bit de decisão para o centro de fusão (decisão do tipo hard) e também o caso em que o detector envia a própria estatística de teste ao centro de fusão (decisão do tipo soft). No âmbito de sensoriamento espectral cooperativo com detecção distribuída, são tratados três cenários diferentes. No primeiro, considera-se o caso em que os rádios cognitivos têm conhecimento a priori do sinal enviado pelo usuário primário do sistema e do canal entre eles e o usuário primário. No segundo caso, há conhecimento apenas do sinal enviado pelo usuário primário. Já no terceiro, os rádios cognitivos não dispõem de qualquer informação a priori do sinal enviado pelo usuário primário. Além do problema de detecção distribuída, a tese também apresenta um capítulo dedicado ao problema de estimação, diretamente associado ao de detecção. Esse último problema é abordado utilizando algoritmos derivados da teoria clássica de filtragem adaptativa. / This doctorate thesis deals with cooperative detection algorithms applied to the spectral sensing problem. The cooperative detection problem is approached under two different paradigms: centralized and distributed detection. In the first case, is considered that a fusion center responsible for detection decision is presented in the system. On the other hand, in the second case, is considered that the cognitive radios in the network exchange information among them. Concerning the centralized spectrum sensing system, the case in which the cognitive radios send only one decision bit (hard decision) to the fusion center and the case in which the detector send the statistic test (soft decision) are considered. Regarding the spectrum sensing system with distributed detection, the work analysis three different scenarios. In the first one, where the cognitive radios explore an a priori knowledge of the primary user signal and the channel between the primary user and the cognitive radio. In the second one, the cognitive radios use an a priori knowledge of only the primary user signal. And, in the las scenario, there is no a priori knowledge about the primary user signal. Besides the distributed detection problem, the thesis also presents a chapter dedicated to the estimation problem, which is directed related to the detection problem. This last issue is approached using adaptive algorithms derived from the classic adaptive filtering theory.