Abstract With the current technological advancements, stage is being set for new ultra-responsive and robust 5G-enabled applications (e.g., virtual reality, Tactile Internet,…) to deliver critical real-time traffic. The emergence of such critical applications requires new networking models that can handle more connected devices with super high reliability and low latency communications. In the view of these research challenges, this thesis aims to propose new techno-economic models and networking paradigms needed in the redesign of wireless network architectures and protocols to support the connectivity requirements by which operators and users effectively benefit from new opportunities introduced by 5G-enabled applications. In this thesis, new paradigms in wireless network access are presented and analyzed. First, dynamic network architecture (DNA) is introduced, where certain classes of wireless terminals can be turned temporarily into an access point (AP) anytime while connected to the Internet. In this concept, a framework is proposed to optimize different aspects of this architecture. Furthermore, to dynamically reconfigure an optimum topology and adjust it to the traffic variations, a new specific encoding of genetic algorithm (GA) is presented. Then, a distributed user-centric spectrum sharing is developed based on DNA networks to enable user-provided access points pervasively share the unused resources. Next, a flexible cloud-based radio access network (FRAN) is proposed to offload traffic to DNA networks in order to provide low latency communications. In the sequel of the thesis, as a new paradigm, a context-aware resource allocation scheme based on adaptive spatial beamforming and reinforcement learning is proposed. In addition, semi-cognitive radio network (SCRN) as a new spectrum sharing model is developed to improve the utility of primary and secondary owners. / Tiivistelmä Nykyaikaisilla teknologisilla edistysaskeleilla mahdollistetaan uusien 5G-pohjaisien erittäin lyhyen vasteajan ja suuren luotettavuuden sovelluksien ilmestyminen kriittisen reaaliaikaisen informaation välittämiseen (esim. taktiiliset ja virtuaalitodellisuus-sovellukset). Näiden kaltaiset sovellukset vaativat uudenlaisia verkottumismalleja, jotka kykenevät käsittelemään enemmän laitteita suurella toimintavarmuudella ja matalalla latenssilla. Tämä väitöskirja ehdottaa näiden haasteiden valossa uusia teknis-taloudellisia malleja ja verkottumisparadigmoja, joita tarvitaan verkkoarkkitehtuurien ja -protokollien uudelleensuunnittelussa tulevaisuuden sovelluksien tarpeet huomioiden, joiden kautta operaattorit ja käyttäjät voivat hyödyntää tulevien 5G-sovelluksien tuomat mahdollisuudet. Tässä väitöskirjassa esitetään ja analysoidaan uusia paradigmoja langattomaan verkkoliityntään. Ensimmäisenä esitellään dynaaminen verkkoarkkitehtuuri (dynamic network architecture, DNA), missä tietyt langattomat terminaalit voidaan väliaikaisesti muuttaa liityntäpisteiksi milloin vain internetyhteyden ollessa käytettävissä. Tämän konseptin puitteissa ehdotetaan viitekehys sen eri osa-alueiden optimoimiseksi. Tämän lisäksi esitetään uusi spesifinen geneettisen algoritmin (GA) koodaus optimaalisen topologian dynaamiseen konfigurointiin ja sen säätämiseen tietoliikenteen määrän mukaan. Tämän jälkeen esitellään kehitetty hajautettu käyttäjäkeskeinen spektrinjako, joka perustuu DNA-verkkoihin ja joka mahdollistaa käyttämättömien resurssien kokonaisvaltaisen jakamisen käyttäjien kautta. Seuraavaksi työssä ehdotetaan joustavaa pilvipalvelu-pohjaista liityntäverkkoa (flexible cloud-based radio access network, FRAN) käyttäjädatan purkamiseksi DNA-verkoille matalalatenssisen tietoliikenteen tarjoamiseksi. Edellä mainittujen menetelmien seurauksena ehdotetaan uutta paradigmaa: Kontekstiriippuvaista resurssien allokointia perustuen adaptiiviseen spatiaaliseen keilanmuodostukseen ja vahvistusoppimiseen. Näiden lisäksi kehitetään uusi spektrinjakomalli puolikognitiivisille radioverkoille (semi-cognitive radio network, SCRN) ensisijaisien ja toissijaisien käyttäjien utiliteetin parantamiseksi.

This thesis proposes a multifaceted production test and post-manufacture yield enhancement framework for RF systems. This framework uses low-cost test and post-manufacture calibration/tuning techniques. Since the test cost and the yield of the RF circuits/sub-system directly contribute to the manufacturing cost of RF systems, the proposed framework minimizes overall RF systems' manufacturing cost by taking two approaches. In the first approach, low-cost testing methodologies are proposed for RF amplifiers and integrated RF substrates with an embedded RF passive filter and interconnect. Techniques are developed to test RF circuits by the analysis of low-frequency signal of the order of few MHz and without using any external RF test-stimulus. Oscillation principles are used to enable testing of RF circuits without any external test-stimulus. In the second approach, to increase the yield of the RF circuits for parametric defects, RF circuits are tuned to compensate for a performance loss during production test using on-board or on-chip resources. This approach includes a diagnosis algorithm to identify faulty circuits within the system, and performs a compensation process that adjusts tunable components to enhance the performance of the RF circuits. In the proposed yield improvement methodologies, the external test stimulus is not required because the stimulus is generated by the RF circuit itself with the help of additional circuitry and faulty circuits are detected using low-cost test methods developed in this research. As a result, the proposed research enables low-cost testing and self-healing of RF systems.

The cognitive radio-based wireless networks have been proposed as a promising technology to improve the utilization of the radio spectrum through opportunistic spectrum access. In this context, the cognitive radios opportunistically access the spectrum which is licensed to primary users when the primary user transmission is detected to be absent. For opportunistic spectrum access, the cognitive radios should sense the radio environment and allocate the spectrum and power based on the sensing results. To this end, in this thesis, I develop a novel cooperative spectrum sensing scheme for cognitive radio networks (CRNs) based on machine learning techniques which are used for pattern classification. In this regard, unsupervised and supervised learning-based classification techniques are implemented for cooperative spectrum sensing. Secondly, I propose a novel joint channel and power allocation scheme for downlink transmission in cellular CRNs. I formulate the downlink resource allocation problem as a generalized spectral-footprint minimization problem. The channel assignment problem for secondary users is solved by applying a modified Hungarian algorithm while the power allocation subproblem is solved by using Lagrangian technique. Specifically, I propose a low-complexity modified Hungarian algorithm for subchannel allocation which exploits the local information in the cost matrix. Finally, I propose a novel dynamic common control channel-based medium access control (MAC) protocol for CRNs. Specifically, unlike the traditional dedicated control channel-based MAC protocols, the proposed MAC protocol eliminates the requirement of a dedicated channel for control information exchange. / October 2015

Cognitive Radio (CR) has received tremendous research attention over the past decade, both in the academia and industry, as it is envisioned as a promising solution to the problem of spectrum scarcity. ACR is a device that senses the spectrum for occupancy by licensed users(also called as primary users), and transmits its data only when the spectrum is sensed to be available. For the efficient utilization of the spectrum while also guaranteeing adequate protection to the licensed user from harmful interference, the CR should be able to sense the spectrum for primary occupancy quickly as well as accurately. This makes Spectrum Sensing(SS) one of the where the goal is to test whether the primary user is inactive(the null or noise-only hypothesis), or not (the alternate or signal-present hypothesis). Computational simplicity, robustness to uncertainties in the knowledge of various noise, signal, and fading parameters, and ability to handle interference or other source of non-Gaussian noise are some of the desirable features of a SS unit in a CR. In many practical applications, CR devices can exploit known structure in the primary signal. IntheIEEE802.22CR standard, the primary signal is a wideband signal, but with a strong narrowband pilot component. In other applications, such as military communications, and blue tooth, the primary signal uses a Frequency Hopping (FH)transmission. These applications can significantly benefit from detection schemes that are tailored for detecting the corresponding primary signals. This thesis develops novel detection schemes and rigorous performance analysis of these primary signals in the presence of fading. For example, in the case of wideband primary signals with a strong narrowband pilot, this thesis answers the further question of whether to use the entire wideband for signal detection, or whether to filter out the pilot signal and use narrowband signal detection. The question is interesting because the fading characteristics of wideband and narrowband signals are fundamentally different. Due to this, it is not obvious which detection scheme will perform better in practical fading environments. At another end of the gamut of SS algorithms, when the CR has no knowledge of the structure or statistics of the primary signal, and when the noise variance is known, Energy Detection (ED) is known to be optimal for SS. However, the performance of the ED is not robust to uncertainties in the noise statistics or under different possible primary signal models. In this case, a natural way to pose the SS problem is as a Goodness-of-Fit Test (GoFT), where the idea is to either accept or reject the noise-only hypothesis. This thesis designs and studies the performance of GoFTs when the noise statistics can even be non-Gaussian, and with heavy tails. Also, the techniques are extended to the cooperative SS scenario where multiple CR nodes record observations using multiple antennas and perform decentralized detection. In this thesis, we study all the issues listed above by considering both single and multiple CR nodes, and evaluating their performance in terms of(a)probability of detection error,(b) sensing-throughput trade off, and(c)probability of rejecting the null-hypothesis. We propose various SS strategies, compare their performance against existing techniques, and discuss their relative advantages and performance tradeoffs. The main contributions of this thesis are as follows: The question of whether to use pilot-based narrowband sensing or wideband sensing is answered using a novel, analytically tractable metric proposed in this thesis called the error exponent with a confidence level. Under a Bayesian framework, obtaining closed form expressions for the optimal detection threshold is difficult. Near-optimal detection thresholds are obtained for most of the commonly encountered fading models. Foran FH primary, using the Fast Fourier Transform (FFT) Averaging Ratio(FAR) algorithm, the sensing-through put trade off are derived in closed form. A GoFT technique based on the statistics of the number of zero-crossings in the observations is proposed, which is robust to uncertainties in the noise statistics, and outperforms existing GoFT-based SS techniques. A multi-dimensional GoFT based on stochastic distances is studied, which pro¬vides better performance compared to some of the existing techniques. A special case, i.e., a test based on the Kullback-Leibler distance is shown to be robust to some uncertainties in the noise process. All of the theoretical results are validated using Monte Carlo simulations. In the case of FH-SS, an implementation of SS using the FAR algorithm on a commercially off-the ¬shelf platform is presented, and the performance recorded using the hardware is shown to corroborate well with the theoretical and simulation-based results. The results in this thesis thus provide a bouquet of SS algorithms that could be useful under different CRSS scenarios.

Relay-assisted cooperative communication exploits spatial diversity to combat wireless fading, and is an appealing technology for next generation wireless systems. Several relay cooperation protocols have been proposed in the literature. In amplify-and-forward (AF)relaying, which is the focus of this thesis, the relay amplifies the signal it receives from the source and forwards it to the destination. AF has been extensively studied in the literature on account of its simplicity since the relay does not need to decode the received signal. We propose a novel optimal relaying policy for two-hop AF cooperative relay systems. In this, an average power-constrained relay adapts its gain and transmit power to minimize the fading-averaged symbol error probability (SEP) at the destination. Next, we consider a generalization of the above policy in which the relay operates as an underlay cognitive radio (CR). This mode of communication is relevant because it promises to address the spectrum shortage constraint. Here, the relay adapts its gain as a function of its local channel gain to the source and destination and also the primary such that the average interference it causes to the primary receiver is also constrained. For both the above policies, we also present near-optimal, simpler relay gain adaptation policies that are easy to implement and that provide insights about the optimal policies. The SEPs and diversity order of the policies are analyzed to quantify their performance. These policies generalize the conventional fixed-power and fixed-gain AF relaying policies considered in cooperative and CR literature, and outperform them by 2.0-7.7 dB. This translates into significant energy savings at the source and relay, and motivates their use in next generation wireless systems.

Cognitive radio (CR) is often described as a context-intelligent radio, capable of changing the transmit parameters dynamically based on the interaction with the environment it operates. The work in this thesis explores the problem of using received signal strength (RSS) measurements taken by a network of CR nodes to generate an interference map of a given geographical area and estimate the locations of multiple primary transmitters that operate simultaneously in the area. A probabilistic model of the problem is developed, and algorithms to address location estimation challenges are proposed. Three approaches are proposed to solve the localization problem. The first approach is based on estimating the locations from the generated interference map when no information about the propagation model or any of its parameters is present. The second approach is based on approximating the maximum likelihood (ML) estimate of the transmitter locations with the grid search method when the model is known and its parameters are available. The third approach also requires the knowledge of model parameters but it is actually based on generating samples from the joint posterior of the unknown location parameter with Markov chain Monte Carlo (MCMC) methods, as an alternative for the highly computationally complex grid search approach. For RF cartography generation problem, we study global and local interpolation techniques, specifically the Delaunay triangulation based techniques as the use of existing triangulation provides a computationally attractive solution. We present a comparative performance evaluation of these interpolation techniques in terms of RF field strength estimation and emitter localization. Even though the estimates obtained from the generated interference maps are less accurate compared to the ML estimator, the rough estimates are utilized to initialize a more accurate algorithm such as the MCMC technique to reduce the complexity of the algorithm. The complexity issues of ML estimators based on full grid search are also addressed by various types of iterative grid search methods. One challenge to apply the ML estimation algorithm to multiple emitter localization problem is that, it requires a pdf approximation to summands of log-normal random variables for likelihood calculations at each grid location. This inspires our investigations on sum of log-normal approximations studied in literature for selecting the appropriate approximation to our model assumptions. As a final extension of this work, we propose our own approximation based on distribution fitting to a set of simulated data and compare our approach with Fenton-Wilkinson's well-known approximation which is a simple and computational efficient approach that fits a log-normal distribution to sum of log-normals by matching the first and second central moments of random variables. We demonstrate that the location estimation accuracy of the grid search technique obtained with our proposed approximation is higher than the one obtained with Fenton-Wilkinson's in many different case scenarios.

The doctoral thesis deals with spectrum sensing in cognitive radio networks (CRN). A number of international organizations are currently actively engaged in standardization of CRN and it points out to the fact that this technology will be widely used in the near future. One of the key features of this technology is a dynamic access to the spectrum, which can be affected by many different harmful factors occurring in the communication chain. The thesis investigates the influence of selected factors on the spectrum sensing process. Another contribution of the work is the optimization of the Kolmogorov - Smirnov statistical test that can be applied for the primary user signal detection. The work also incorporates the analysis of the influence of the harmful effects caused by the commonly used transmitters and receivers on various spectrum sensing methods. The investigations are verified by the results of the simulations and also by the measurements with experimental platforms based on the software-defined radio (SDR).

Vu que la modulation multi-porteuses est largement utilisée dans les communications sans fil et la radio cognitive (CR pour “Cognitive Radio”) améliore l’utilisation des ressources radio et du spectre, nous nous concentrons sur les réseaux radio cognitifs (CR) pour faire progresser l’allocation des ressources, le routage, et l’ajustement de la puissance d’émission vers les récepteurs (synthèse de faisceaux ou beamforming) dans cette thèse. Nous étudions deux types de modulations multi-porteuses :Orthogonal Frequency-Division Multiplexing (OFDM) à base d’ondelettes (WOFDM pourWavelet OFDM) et OFDM dans sa forme classique ou traditionnelle (OFDM s’appuyant sur la transformation de Fourier pour partager les ressources). WOFDM adopte Wavelet Packet Modulation (WPM) pour obtenir des lobes secondaires beaucoup plus faibles dans la densité spectrale de puissance du signal transmis en comparaison à OFDM. WPM permet de surcroit à WOFDM de s’affranchir du Préfixe Cyclique (indispensable à OFDM) et d’exploiter l’égalisation pour combattre l’Interférence entre Symboles (ISI). Nous évaluons la performance de WOFDM sous différentes conditions du canal radio. Nous comparons la performance de WOFDM, qui s’appuie sur l’égalisation dans le domaine temporel, à celle de OFDM, qui requiert l’utilisation du Préfixe Cyclique et opère dans le domaine fréquentiel / In view of the wide usage of multicarrier modulation in wireless communications and the prominent contribution of Cognitive Radio (CR) to deal with critical shortage of spectrum resource, we focus on multicarrier based cognitive radio networks to investigate general resource allocation issues: subcarrier allocation, power allocation, routing, and beamforming in this thesis. We investigate two types of multicarrier modulation: Wavelet-based Orthogonal Frequency Division Multiplexing (WOFDM) and Fourier-based Orthogonal Frequency Division Multiplexing (OFDM). WOFDM adopts Wavelet Packet Modulation (WPM). Compared with fourier-based OFDM, wavelet-based OFDM achieves much lower side lobe in the transmitted signal. Wavelet-based OFDM excludes Cyclic Prefix (CP) which is used in fourier-based OFDM systems. Wavelet-based OFDM turns to exploit equalization to combat Inter-Symbol Interference (ISI). We evaluate the performance of WOFDM under different channel conditions. We compare the performance of wavelet-based OFDM using equalization in the time domain to that of fourier-based OFDM with CP and the equalization in the frequency domain

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