Abstract In this thesis, novel data and channel estimation methods are proposed and analyzed for low-complexity short-range communication (SRC) radios. Low complexity is challenging to achieve especially in very wideband or millimeter-wave SRC radios where phase recovery and energy capture from numerous multipaths easily become a bottleneck for system design. A specific type of transceiver is selected using pulse amplitude modulation (PAM) at the transmitter and energy detection (ED) at the receiver, and it is thus called an ED-PAM system. Nonnegative PAM alphabets allow using an ED structure which enables a phase-unaware detection method for avoiding complicated phase recovery at the receiver. Moreover, the ED-PAM approach results in a simple multipath energy capture, and only one real decision variable, whose dimension is independent of the symbol alphabet size, is needed. In comparison with optimal phase-aware detection, the appealing simplicity of suboptimal ED-PAM systems is achieved at the cost of the need for a higher transmitted signal energy or shorter link distance for obtaining a sufficient signal-to-noise ratio (SNR) at the receiver, as ED-PAM systems are more vulnerable to the effects of noise and interference. On the other hand, the consequences of requiring a higher SNR may not be severe in the type of SRC scenarios where a sufficient received SNR is readily available due to a short link distance. Furthermore, significant interference can be avoided by signal design. However, what has slowed down the development of ED-PAM systems is that efficient symbol decision threshold estimation and related error probability analysis in multipath fading channels have remained as unsolved problems. Based on the above observations, this thesis contributes to the state-of-the-art of the design and analysis for ED-PAM systems as follows. Firstly, a closed-form near-optimal decision threshold selection method, which adapts to a time-varying channel gain and enables an arbitrary choice of the PAM alphabet size and an integer time-bandwidth product of the receiver filters, is proposed. Secondly, two blind estimation schemes of the parameters for the threshold estimation are introduced. Thirdly, analytical error probability evaluation in frequency-selective multipath fading channels is addressed. Special attention is given to lognormal fading channels, which are typically used to model very wideband SRC multipath channels. Finally, analytical error probability evaluation with nonideal parameter estimation is presented. The results can be used in designing low-complexity transceivers for very wideband and millimeter-wave wireless SRC devices of the future. / Tiivistelmä Tässä työssä esitetään ja analysoidaan uusia data- ja kanavaestimointimenetelmiä, joiden tavoitteena on yksinkertaistaa lähikommunikaatiota (short-range communication, SRC) langattomien laitteiden välillä. SRC-radioiden yksinkertainen toteutus on poikkeuksellisen haasteellista silloin, kun käytetään erittäin suurta kaistanleveyttä tai millimetriaaltoalueen tiedonsiirtoa. Tällöin vastaanottimen yksinkertaisen toteutuksen voivat estää esimerkiksi kantoaallon vaiheen estimointi ja signaalienergian kerääminen lukuisilta kanavan monitiekomponenteilta. Näistä lähtökohdista valitaan SRC-radion järjestelmämalliksi positiiviseen pulssiamplitudimodulaatioon (pulse amplitude modulation, PAM) perustuva lähetin ja energiailmaisimeen (energy detection, ED) perustuva vastaanotin. ED-PAM-järjestelmän ei tarvitse tietää vastaanotetun signaalin vaihetta ja signaalienergian kerääminen tapahtuu yksinkertaisen diversiteettiyhdistelytekniikan avulla. Lisäksi ilmaisuun tarvitaan vain yksi reaalinen päätösmuuttuja, jonka dimensio on riippumaton PAM-tasojen määrästä. ED-PAM-tekniikan yksinkertaisuutta optimaaliseen vaihetietoiseen ilmaisuun verrattuna ei saavuteta ilmaiseksi. Yhtenä rajoituksena on alioptimaalisen ED-PAM-tekniikan luontainen taipumus vahvistaa kohinan ja häiriöiden vaikutusta symbolin päätöksenteossa. Kohinan vahvistus ei välttämättä ole suuri ongelma niissä SRC-radioissa, joissa pienen linkkietäisyyden johdosta riittävä signaali-kohinasuhde vastaanottimessa voidaan kohinan vahvistuksesta huolimatta saavuttaa. Myös häiriöiden vahvistuksen vaikutusta voidaan tehokkaasti vähentää signaalisuunnittelulla. Joka tapauksessa ED-PAM-tekniikan käyttöönottoa on hidastanut tehokkaiden symbolipäätöskynnysten estimointi- ja analysointimenetelmien puuttuminen. Edellä mainitut havainnot ovat motivoineet löytämään uusia suunnittelu- ja analyysimenetelmiä ED-PAM-järjestelmille seuraavasti. Symbolipäätöskynnysten estimointiin johdetaan lähes optimaalinen suljetun muodon menetelmä, joka kykenee adaptoitumaan muuttuvassa kanavassa ja valitsemaan mielivaltaisen kokonaisluvun sekä PAM-tasojen määrälle että vastaanottimen aika-kaistanleveystulolle. Lisäksi esitetään kaksi sokeaa päätöskynnysten estimointimenetelmää, jotka eivät tarvitse redundanttista opetussignaalia. Työn toisessa osassa ED-PAM-järjestelmän symbolivirhesuhdetta analysoidaan taajuusselektiivisessä monitiekanavassa. Analyysissä keskitytään log-normaalijakauman mukaan häipyvään kanavaan. Seuraavaksi analyysia laajennetaan ottamalla mukaan epäideaalisten kynnysarvojen estimoinnin vaikutus. Saavutettuja tuloksia voidaan hyödyntää erittäin laajakaistaisten ja millimetriaaltoalueen SRC-laitteiden suunnittelussa.

The popularity of the wireless networks is so great that we will soon reach the point where most of the devices work based on that, but new challenges in wireless channel access will be created with these increasingly widespread wireless communications. Multi-channel CSMA protocols have been designed to enhance the throughput of the next generation wireless networks compared to single-channel protocols. However, their performance analysis still needs careful considerations. In this thesis, a set of techniques are proposed to model and analyse the CSMA protocols in terms of channel sensing and channel access. In that respect, the performance analysis of un-slotted multi-channel CSMA protocols is studied through considering the hidden terminals. In the modelling phase, important parameters such as shadowing and path loss impairments are being considered. Following that, due to the high importance of spectrum sensing in CSMA protocols, the Double-Threshold Energy Detector (DTED) is thoroughly investigated in this thesis. An iterative algorithm is also proposed to determine optimum values of detection parameters in a sensing-throughput problem formulation. Vehicle-to-Roadside (V2R) communication, as a part of Intelligent Transportation System (ITS), over multi-channel wireless networks is also modelled and analysed in this thesis. In this respect, through proposing a novel mathematical model, the connectivity level which an arbitrary vehicle experiences during its packet transmission with a RSU is also investigated.

The master’s thesis deals with development of the radio channel analyser operating in the unlicensed 2.4 GHz band according to IEEE 802.15.4 standard. The analyzer will contain two basic functions, namely energy detection of channels and analysation of selected channel. Detection of energy will be solved by selection between simple graph and text list on display. Based on information about the occupancy of channels the user can decide which channel would be set up. During the receiving of valid frame the source address, signal strength and signal quality will be stored. At the end the processor will show these data on display. For controlling the device will be installed two LEDs and four buttons.

One of the problems with the modern radio communication is the lack of availableradio frequencies. Recent studies have shown that, while the available licensed radiospectrum becomes more occupied, the assigned spectrum is significantly underutilized.To alleviate the situation, cognitive radio (CR) technology has been proposedto provide an opportunistic access to the licensed spectrum areas. Secondary CRsystems need to cyclically detect the presence of a primary user by continuouslysensing the spectrum area of interest. Radiowave propagation effects like fading andshadowing often complicate sensing of spectrum holes. When spectrum sensing isperformed in a cooperative manner, then the resulting sensing performance can beimproved and stabilized. In this thesis, two fully distributed and adaptive cooperative Primary User (PU)detection solutions for CR networks are studied. In the first part of this thesis we study a distributed energy detection schemewithout using any fusion center. Due to reduced communication such a topologyis more energy efficient. We propose the usage of distributed, diffusion least meansquare (LMS) type of power estimation algorithms with different network topologies.We analyze the resulting energy detection performance by using a commonframework and verify the theoretical findings through simulations. In the second part of this thesis we propose a fully distributed detection scheme,based on the largest eigenvalue of adaptively estimated correlation matrices, assumingthat the primary user signal is temporally correlated. Different forms of diffusionLMS algorithms are used for estimating and averaging the correlation matrices overthe CR network. The resulting detection performance is analyzed using a commonframework. In order to obtain analytic results on the detection performance, theadaptive correlation matrix estimates are approximated by a Wishart distribution.The theoretical findings are verified through simulations. / <p>QC 20170908</p>

L’allocation des ressources spectrales à des services de communications sans fil, sans cesse plus nombreux et plus gourmands, a récemment mené la communauté radio à vouloir remettre en question la stratégie de répartition des bandes de fréquences imposée depuis plus d’un siècle. En effet une étude rendue publique en 2002 par la commission fédérale des communications aux Etats-Unis (Federal Communications Commission - FCC) mit en évidence une pénurie des ressources spectrales dans une large bande de fréquences comprise entre quelques mégahertz à plusieurs gigahertz. Cependant, cette même étude expliqua cette pénurie par une allocation statique des ressources aux différents services demandeurs plutôt que par une saturation des bandes de fréquences. Cette explication fut par la suite corroborée par de nombreuses mesures d’occupation spectrale, réalisées dans plusieurs pays, qui montrèrent une forte sous-utilisation des bandes de fréquences en fonction du temps et de l’espace, représentant par conséquent autant d’opportunité spectrale inexploitée. Ces constations donnèrent naissance à un domaine en plein effervescence connu sous le nom d’Accès Opportuniste au Spectre (Opportunistic Spectrum Access). Nos travaux suggèrent l’étude de mécanismes d’apprentissage pour la radio intelligente (Cognitive Radio) dans le cadre de l’Accès Opportuniste au Spectre (AOS) afin de permettre à des équipements radio d’exploiter ces opportunités de manière autonome. Pour cela, nous montrons que les problématiques d’AOS peuvent être fidèlement représentées par des modèles d’apprentissage par renforcement. Ainsi, l’équipement radio est modélisé par un agent intelligent capable d’interagir avec son environnement afin d’en collecter des informations. Ces dernières servent à reconnaître, au fur et à mesure des expériences, les meilleurs choix (bandes de fréquences, configurations, etc.) qui s’offrent au système de communication. Nous nous intéressons au modèle particulier des bandits manchots (Multi-Armed Bandit appliqué à l’AOS). Nous discutons, lors d’une phase préliminaire, différentes solutions empruntées au domaine de l’apprentissage machine (Machine Learning). Ensuite, nous élargissons ces résultats à des cadres adaptés à la radio intelligente. Notamment, nous évaluons les performances de ces algorithmes dans le cas de réseaux d’équipements qui collaborent en prenant en compte, dans le modèle suggéré, les erreurs d’observations. On montre de plus que ces algorithmes n’ont pas besoin de connaître la fréquence des erreurs d’observation afin de converger. La vitesse de convergence dépend néanmoins de ces fréquences. Dans un second temps nous concevons un nouvel algorithme d’apprentissage destiné à répondre à des problèmes d’exploitation des ressources spectrales dans des conditions dites de fading. Tous ces travaux présupposent néanmoins la capacité de l’équipement intelligent à détecter efficacement l’activité d’autres utilisateurs sur la bande (utilisateurs prioritaires dits utilisateurs primaires). La principale difficulté réside dans le fait que l’équipement intelligent ne suppose aucune connaissance a priori sur son environnement (niveau du bruit notamment) ou sur les utilisateurs primaires. Afin de lever le doute sur l’efficacité de l’approche suggérée, nous analysons l’impact de ces incertitudes sur le détecteur d’énergie. Ce dernier prend donc le rôle d’observateur et envoie ses observations aux algorithmes d’apprentissage. Nous montrons ainsi qu’il est possible de quantifier les performances de ce détecteur dans des conditions d’incertitude sur le niveau du bruit ce qui le rend utilisable dans le contexte de la radio intelligente. Par conséquent, les algorithmes d’apprentissage utilisés pourront exploiter les résultats du détecteur malgré l’incertitude inhérente liée à l’environnement considéré et aux hypothèses (sévères) d’incertitude liées au problème analysé. / During the last century, most of the meaningful frequency bands were licensed to emerging wireless applications. Because of the static model of frequency allocation, the growing number of spectrum demanding services led to a spectrum scarcity. However, recently, series of measurements on the spectrum utilization showed that the different frequency bands were underutilized (sometimes even unoccupied) and thus that the scarcity of the spectrum resource is virtual and only due to the static allocation of the different bands to specific wireless services. Moreover, the underutilization of the spectrum resource varies on different scales in time and space offering many opportunities to an unlicensed user or network to access the spectrum. Cognitive Radio (CR) and Opportunistic Spectrum Access (OSA) were introduced as possible solutions to alleviate the spectrum scarcity issue.In this dissertation, we aim at enabling CR equipments to exploit autonomously communication opportunities found in their vicinity. For that purpose, we suggest decision making mechanisms designed and/or adapted to answer CR related problems in general, and more specifically, OSA related scenarios. Thus, we argue that OSA scenarios can be modeled as Multi-Armed Bandit (MAB) problems. As a matter of fact, within OSA contexts, CR equipments are assumed to have no prior knowledge on their environment. Acquiring the necessary information relies on a sequential interaction between the CR equipment and its environment. Finally, the CR equipment is modeled as a cognitive agent whose purpose is to learn while providing an improving service to its user. Thus, firstly we analyze the performance of UCB1 algorithm when dealing with OSA problems with imperfect sensing. More specifically, we show that UCB1 can efficiently cope with sensing errors. We prove its convergence to the optimal channel and quantify its loss of performance compared to the case with perfect sensing. Secondly, we combine UCB1 algorithm with collaborative and coordination mechanism to model a secondary network (i.e. several SUs). We show that within this complex scenario, a coordinated learning mechanism can lead to efficient secondary networks. These scenarios assume that a SU can efficiently detect incumbent users’ activity while having no prior knowledge on their characteristics. Usually, energy detection is suggested as a possible approach to handle such task. Unfortunately, energy detection in known to perform poorly when dealing with uncertainty. Consequently, we ventured in this Ph.D. to revisit the problem of energy detection limits under uncertainty. We present new results on its performances as well as its limits when the noise level is uncertain and the uncertainty is modeled by a log-normal distribution (as suggested by Alexander Sonnenschein and Philip M. Fishman in 1992). Within OSA contexts, we address a final problem where a sensor aims at quantifying the quality of a channel in fading environments. In such contexts, UCB1 algorithms seem to fail. Consequently, we designed a new algorithm called Multiplicative UCB (UCB) and prove its convergence. Moreover, we prove that MUCB algorithms are order optimal (i.e., the order of their learning rate is optimal). This last work provides a contribution that goes beyond CR and OSA. As a matter of fact, MUCB algorithms are introduced and solved within a general MAB framework.

Die extrem große Bandbreite, die UWB-Systeme zur Übertragung von Daten nutzen können, ermöglicht theoretisch eine sehr hohe Datenrate. Eine mögliche Umsetzung der UWB-Technologie ist die sogenannte Multiband-Impuls-Radio-Architektur (MIRA). Dieses UWB-System basiert auf der Übertragung von Daten mittels kurzer Impulse parallel in mehreren Frequenzbändern. Als Empfänger kommen einfache Energiedetektoren zum Einsatz. Diese Komponenten haben entscheidenden Einfluss auf die Leistungsfähigkeit des gesamten Systems. Deshalb liegt der Schwerpunkt dieser Arbeit auf der Untersuchung der Leistungsfähigkeit und im speziellen der Herleitung der Bitfehlerwahrscheinlichkeiten für Energiedetektoren in unterschiedlichen UWB-Kanälen. Aufgrund des sehr einfachen Aufbaus eines Energiedetektors wird dieser auch in vielen anderen Bereichen eingesetzt. So werden Energiedetektoren zur Detektion von freien Bereichen im Übertragungsspektrum bei Cognitive Radio und für weitere unterschiedliche Übertragungssysteme wie z.B. Sensorsysteme mit geringer Datenrate und Übertragungssysteme die zusätzlich Ortung ermöglichen, genutzt.

Due to the highly-varying wireless channels over time, frequency, and space domains, statistical QoS provisioning, instead of deterministic QoS guarantees, has become a recognized feature in the next-generation wireless networks. In this dissertation, we study the adaptive wireless resource allocation problems for statistical QoS provisioning, such as guaranteeing the specified delay-bound violation probability, upper-bounding the average loss-rate, optimizing the average goodput/throughput, etc., in several typical types of mobile wireless networks. In the first part of this dissertation, we study the statistical QoS provisioning for mobile multicast through the adaptive resource allocations, where different multicast receivers attempt to receive the common messages from a single base-station sender over broadcast fading channels. Because of the heterogeneous fading across different multicast receivers, both instantaneously and statistically, how to design the efficient adaptive rate control and resource allocation for wireless multicast is a widely cited open problem. We first study the time-sharing based goodput-optimization problem for non-realtime multicast services. Then, to more comprehensively characterize the QoS provisioning problems for mobile multicast with diverse QoS requirements, we further integrate the statistical delay-QoS control techniques — effective capacity theory, statistical loss-rate control, and information theory to propose a QoS-driven optimization framework. Applying this framework and solving for the corresponding optimization problem, we identify the optimal tradeoff among statistical delay-QoS requirements, sustainable traffic load, and the average loss rate through the adaptive resource allocations and queue management. Furthermore, we study the adaptive resource allocation problems for multi-layer video multicast to satisfy diverse statistical delay and loss QoS requirements over different video layers. In addition, we derive the efficient adaptive erasure-correction coding scheme for the packet-level multicast, where the erasure-correction code is dynamically constructed based on multicast receivers’ packet-loss statuses, to achieve high error-control efficiency in mobile multicast networks. In the second part of this dissertation, we design the adaptive resource allocation schemes for QoS provisioning in unicast based wireless networks, with emphasis on statistical delay-QoS guarantees. First, we develop the QoS-driven time-slot and power allocation schemes for multi-user downlink transmissions (with independent messages) in cellular networks to maximize the delay-QoS-constrained sum system throughput. Second, we propose the delay-QoS-aware base-station selection schemes in distributed multiple-input-multiple-output systems. Third, we study the queueaware spectrum sensing in cognitive radio networks for statistical delay-QoS provisioning. Analyses and simulations are presented to show the advantages of our proposed schemes and the impact of delay-QoS requirements on adaptive resource allocations in various environments.

The doctoral thesis deals with spectrum sensing and subsequent use of the frequency spectrum by multicarrier communication system, which parameters are set on the basis of the optimization technique. Adaptation settings can be made with respect to several requirements as well as state and occupancy of individual communication channels. The system, which is characterized above is often referred as cognitive radio. Equipments operating on cognitive radio principles will be widely used in the near future, because of frequency spectrum limitation. One of the main contributions of the work is the novel usage of the Kolmogorov – Smirnov statistical test as an alternative detection of primary user signal presence. The new fitness function for Particle Swarm Optimization (PSO) has been introduced and the Error Vector Magnitude (EVM) parameter has been used in the adaptive greedy algorithm and PSO optimization. The dissertation thesis also incorporates information about the reliability of the frequency spectrum sensing in the modified greedy algorithm. The proposed methods are verified by the simulations and the frequency domain energy detection is implemented on the development board with FPGA.