Resource allocation consists in allocating spectrum and power on every link of a network, possibly under power and rate requirements. In the context of cognitive radios, almost 15 years of research produced an impressive amount of theoretical contributions, exploring a wide range of possibilities. However, despite the ever-growing list of imaginable scenarios, we observe in Chapter 2 that most of these studies are based on similar working hypotheses. Our first contribution is to challenge some of these hypotheses, and propose a novel resource allocation scheme. Sticking to realistic assumptions, we show how our scheme reduces both computational complexity and control traffic, compared to other state-of-the-art techniques.<p><p>Due to a majority of the abovementioned studies making some constraining assumptions, realistic system designs and experimental demonstrations are much more quiet and unharvested fields. In an effort to help this transition from theory to practice, our second contribution is a four-nodes cognitive network demonstrator, presented in Chapter 3. In particular, we aim at providing a modular platform available for further open collaboration: different options for spectrum sensing, resource allocation, synchronisation and others can be experimented on this demonstrator. As an example, we develop a simple protocol to show that our proposed resource allocation scheme is fully implementable, and that primary users can be avoided using our approach.<p><p>Chapter 4 aims at removing another working hypothesis made when developping our resource allocation scheme. Indeed, resource alloca- tion is traditionally a Media Access Control (MAC) layer problem. This means that when solving a resource allocation problem in a network, the routing paths are usually assumed to be known. Conversely, the routing problem, which is a network layer issue, usually assumes that the available capacities on each link of the network (which depend on resource allocation) are known. Nevertheless, these two problems are mathematically entangled, and a cross-layer allocation strategy can best decoupled approaches in several ways, as we discuss in Chapter 4. Accordingly, our third and last contribution is to develop such a cross-layer allocation scheme for the scenario proposed in previous chapters.<p><p>All conclusions are summarised in Chapter 5, which also points to a few tracks for future research. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

COOPERATIVE WIDEBAND SPECTRUM SENSING BASED ON JOINT SPARSITY By Ghazaleh Jowkar, Master of Science A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University Virginia Commonwealth University 2017 Major Director: Dr. Ruixin Niu, Associate Professor of Department of Electrical and Computer Engineering In this thesis, the problem of wideband spectrum sensing in cognitive radio (CR) networks using sub-Nyquist sampling and sparse signal processing techniques is investigated. To mitigate multi-path fading, it is assumed that a group of spatially dispersed SUs collaborate for wideband spectrum sensing, to determine whether or not a channel is occupied by a primary user (PU). Due to the underutilization of the spectrum by the PUs, the spectrum matrix has only a small number of non-zero rows. In existing state-of-the-art approaches, the spectrum sensing problem was solved using the low-rank matrix completion technique involving matrix nuclear-norm minimization. Motivated by the fact that the spectrum matrix is not only low-rank, but also sparse, a spectrum sensing approach is proposed based on minimizing a mixed-norm of the spectrum matrix instead of low-rank matrix completion to promote the joint sparsity among the column vectors of the spectrum matrix. Simulation results are obtained, which demonstrate that the proposed mixed-norm minimization approach outperforms the low-rank matrix completion based approach, in terms of the PU detection performance. Further we used mixed-norm minimization model in multi time frame detection. Simulation results shows that increasing the number of time frames will increase the detection performance, however, by increasing the number of time frames after a number of times the performance decrease dramatically.

Abstract The aim of this thesis is to devise coordination and adaptation techniques that enable the wireless devices operating in a cognitive network to utilize their available resources efficiently. The first part of this thesis considers the case where multiple autonomous devices sense the frequency channels sequentially in some sensing order for spectrum opportunities. In particular, the first part is interested in the scenario where devices with false alarms autonomously select the sensing orders in which they visit channels, without coordination from a centralized entity. An adaptive persistent sensing order selection strategy that allows autonomous adaptations to collision-free sensing orders is proposed and evaluated. It is shown that the proposed strategy converges and maximizes cognitive network throughput compared to a random selection of sensing orders. The second part of this thesis considers the case where distributed devices interact with one another to cooperate to fulfill tasks or to improve the efficiency of network resource usage. Tools from coalition formation game theory are adopted to devise dynamic cooperative strategies for distributed devices. Dynamic coalition formation methods, are proposed for two different network scenarios: 1) Distributed devices operating in an interference channel; 2) Distributed devices performing spectrum sensing. It is observed that in distributed spectrum sensing if the devices pursue their goals selfishly then coalition formation may lead to a suboptimal equilibrium where devices, through their interactions, reach an undesirable coalition structure from a network point of view. The proposed selfish model of dynamic coalition formation is then extended to determine whether and how the coalitional behavior of devices will change if coalition formation is ''not entirely selfish''. It is observed that for the problem of distributed spectrum sensing, average throughput per device is increased when devices cooperate to maximize the overall gains of the group as compared to when they cooperate to increase their individual gains. Finally, in the last part of the thesis, to reduce spectrum sensing overhead and total energy consumption of a cognitive radio network, the problem of sensor selection is considered. Different techniques for selecting devices with the best detection performance are proposed, and it is shown that the proposed device selection methods are able to offer significant gains in terms of system performance as compared to a random selection of devices. / Tiivistelmä Tämän työn tavoitteena oli kehittää koordinointi- ja adaptointimenetelmiä, jotka mahdollistavat langattomien laitteiden toiminnan kognitiivisessa verkossa ja tarjolla olevien resurssien tehokkaan käytön. Työn ensimmäisessä osassa käsitellään tapausta, jossa useat itsenäiset laitteet havainnoivat taajuuskanavien spektriominaisuuksia sekventiaalisesti jossakin järjestyksessä. Ensimmäisessä osassa ollaan erityisesti kiinnostuneita skenaariosta, jossa virheellisen hälytyksen antava laite automaattisesti valitsee kanavien havainnointijärjestyksen, joka tapahtuu ilman keskusyksikön koordinointia. Tässä työssä ehdotetaan ja evaluoidaan adaptiivinen jatkuva havainnointijärjestyksen valintastrategia, joka sallii itsenäisen sopeutumisen törmäysvapaaseen havainnointijärjestykseen. Osoitetaan, että ehdotettu strategia suppenee ja maksimoi kognitiivisen verkon kapasiteetin verrattuna satunnaiseen havainnointijärjestysten valintaan. Työn toisessa osassa pohditaan tilannetta, jossa hajautetut laitteet vuorovaikuttavat keskenään yhteistyössä suorittaakseen tehtäviään tai parantaakseen verkon resurssien käytön tehokkuutta. Peliteoreettisia työkaluja koalitioiden muodostamiseen mukautetaan dynaamisten yhteistoiminnallisten strategioiden laatimiseen hajautetuille laitteille. Dynaamisia koalitioiden muodostamismenetelmiä ehdotetaan kahteen erilaiseen verkkoskenaarioon: 1) hajautetut laitteet toimivat häiriöllisessä kanavassa, 2) hajautetut laitteet suorittavat spektrin havainnointia. Havaitaan, että jos hajautetussa spektrin havainnoinnissa laitteet tavoittelevat päämääriään itsekkäästi, niin koalitioiden muodostaminen voi johtaa alioptimaaliseen tasapainotilaan, jossa laitteet keskinäisen vaikutuksensa kautta saavuttavat verkon näkökulmasta epätoivotun koalitiorakenteen. Ehdotettua itsekästä mallia dynaamiseen koalitioiden muodostamiseen laajennetaan ottamaan selville, miten laitteiden koalitiokäyttäytyminen muuttuu, jos koalitioiden muodostaminen ei ole täydellisen itsekästä. Havaitaan, että hajautetun spektrin havainnoinnin probleemassa, keskimääräinen laitekohtainen kapasiteetti kasvaa kun laitteet tekevät yhteistyötä maksimoidakseen ryhmän kokonaishyödyn verrattuna siihen, jos ne tekevät yhteistyötä lisätäkseen yksittäisiä etujaan. Työn viimeisessä osassa pohditaan sensorien valintaongelmaa. Siinä ehdotetaan erilaisia menetelmiä, jotka valitsevat parhaan suorituskyvyn omaavat laitteet ja näytetään, että ehdotetut laitteiden valintamenetelmät pystyvät tarjoamaan merkittäviä suorituskykyetuja verrattuna satunnaiseen laitteiden valintaan.

[pt] A tecnologia de rádio cognitivo está em pleno desenvolvimento na academia e indústria, sendo apresentada como uma solução para o reduzir o congestionamento do espectro radioelétrico. Dessa forma, diversos estudos têm sido desenvolvidos para obter novas técnicas de compartilhamento do espectro entre usuários ditos primários e secundários. Estas técnicas devem ser robustas o suficiente para minimizar as colisões de ocupação do espectro entre os usuários supracitados, quando o acesso dinâmico ao espectro for aplicado. O presente estudo investigou as soluções de ocupação compartilhada do espectro, em especial nos para serviços de voz na faixa de 450 MHz. A modelagem de ocupação dos canais, a partir de medidas de transmissões reais, permitiu o desenvolvimento de algoritmo robusto que realiza a predição de espaços espectrais (white spaces) dentro de canais destinados a usuários primários. Esse método proposto define, estatisticamente, uma janela de intervalos de tempo futuros que pode ser utilizada por usuários secundários, por apresentar maior probabilidade de possuir espaços espectrais livres, minimizando as possíveis colisões. O emprego do método proposto aumenta a vazão de informações de modo seguro e,com alto desempenho, otimizando,assim,a utilização do espectro radioelétrico. / [en] The cognitive radio technology is being developedin universities and industry as a solution to the radio spectrum scarcity. This technology willallow spectrum sharing between primary and secondary telecommunication users. The techniques employed must be robust enough to minimize spectrum occupancy collisions, when the dynamic spectrum access is applied. This study investigates the trends of spectrum usersoccupation, particularly in voice services in the 450 MHz frequency band.An users occupancy model was developed taking into accountmeasured data of real transmissions. It allowed the development of a robust algorithm that predicts spectral vacancy in channels allocated to primary users. The method selects, statistically, a group of future time intervals that can be used by secondary users, due to a higher probability of having a free spectral space. The use of this new technique minimizes possible collisions, increasing the flow of information in secure way and optimizing the radio spectrum use.

Nous proposons un nouveau schéma d’échantillonnage non uniforme périodique appelé Système d’Échantillonnage Non Uniforme en Radio Intelligente (SENURI). Notre schéma détecte la localisation spectrale des bandes actives dans la bande totale échantillonnée afin de réduire la fréquence moyenne d’échantillonnage, le nombre d’échantillons prélevé et par conséquent la consommation d’énergie au niveau du traitement numérique. La fréquence moyenne d’échantillonnage du SENURI dépend uniquement du nombre de bandes contenues dans le signal d’entrée x(t). Il est nettement plus performant, en termes d’erreur quadratique, qu’une architecture classique d’échantillonnage non uniforme périodique constituée de p branches, lorsque le spectre de x(t) change dynamiquement. / In this work we consider the problem of designing an effective sampling scheme for sparse multi-band signals. Based on previous results on periodic non-uniform sampling (Multi-Coset) and using the well known Non-Uniform Fourier Transform through Bartlett’s method for Power Spectral Density estimation, we propose a new sampling scheme named the Dynamic Single Branch Non-uniform Sampler (DSB-NUS). The idea of the proposed scheme is to reduce the average sampling frequency, the number of samples collected, and consequently the power consumption of the Analog to Digital Converter (ADC). In addition to that our proposed method detects the location of the bands in order to adapt the sampling rate. In this thesis, we show through simulation results that compared to existing multi-coset based samplers, our proposed sampler provides superior performance, both in terms of sampling rate and energy consumption. It is notconstrained by the inflexibility of hardware circuitry and is easily reconfigurable. We also show the effect of the false detection of active bands on the average sampling rate of our new adaptive non-uniform sub-Nyquist sampler scheme.

La thèse aborde le problème de la détection d’un signal dans une bande de fréquences donnée sans aucune connaissance à priori sur la source (détection aveugle) dans le contexte de la radio intelligente. Le détecteur proposé dans la thèse est basé sur l’estimation des valeurs propres de la matrice de corrélation du signal reçu. A partir de ces valeurs propres, plusieurs critères ont été développés théoriquement (Standard Condition Number, Scaled Largest Eigenvalue, Largest Eigenvalue) en prenant pour hypothèse majeure un nombre fini d’éléments, contrairement aux hypothèses courantes de la théorie des matrices aléatoires qui considère un comportement asymptotique de ces critères. Les paramètres clés des détecteurs ont été formulés mathématiquement (probabilité de fausse alarme, densité de probabilité) et une correspondance avec la densité GEV a été explicitée. Enfin, ce travail a été étendu au cas multi-antennes (MIMO) pour les détecteurs SLE et SCN. / In Cognitive Radio, Spectrum Sensing (SS) is the task of obtaining awareness about the spectrum usage. Mainly it concerns two scenarios of detection: (i) detecting the absence of the Primary User (PU) in a licensed spectrum in order to use it and (ii) detecting the presence of the PU to avoid interference. Several SS techniques were proposed in the literature. Among these, Eigenvalue Based Detector (EBD) has been proposed as a precious totally-blind detector that exploits the spacial diversity, overcome noise uncertainty challenges and performs adequately even in low SNR conditions. The first part of this study concerns the Standard Condition Number (SCN) detector and the Scaled Largest Eigenvalue (SLE) detector. We derived exact expressions for the Probability Density Function (PDF) and the Cumulative Distribution Function (CDF) of the SCN using results from finite Random Matrix Theory; In addition, we derived exact expressions for the moments of the SCN and we proposed a new approximation based on the Generalized Extreme Value (GEV) distribution. Moreover, using results from the asymptotic RMT we further provided a simple forms for the central moments of the SCN and we end up with a simple and accurate expression for the CDF, PDF, Probability of False-Alarm, Probability of Detection, of Miss-Detection and the decision threshold that could be computed and hence provide a dynamic SCN detector that could dynamically change the threshold value depending on target performance and environmental conditions. The second part of this study concerns the massive MIMO technology and how to exploit the large number of antennas for SS and CRs. Two antenna exploitation scenarios are studied: (i) Full antenna exploitation and (ii) Partial antenna exploitation in which we have two options: (i) Fixed use or (ii) Dynamic use of the antennas. We considered the Largest Eigenvalue (LE) detector if noise power is perfectly known and the SCN and SLE detectors when noise uncertainty exists.

In this thesis, we investigate two different system infrastructures in underlay cognitive radio network, in which two popular techniques, cross-layer design and cooperative communication, are considered, respectively. In particular, we introduce the Aggressive Adaptive Modulation and Coding (A-AMC) into the cross-layer design and achieve the optimal boundary points in closed form to choose the AMC and A-AMC transmission modes by taking into account the Channel State Information (CSI) from the secondary transmitter to both the primary receiver and the secondary receiver. What’s more, for the cooperative communication design, we consider three different relay selection schemes: Partial Relay Selection, Opportunistic Relay Selection and Threshold Relay Selection. The Probability Density Functions (PDFs) of the Signal-to- Noise Ratio (SNR) in each hop for different selection schemes are provided, and then the exact closed-form expressions for the end-to-end packet loss rate in the secondary link considering the cooperation of the Decode-and-Forward (DF) relay for different relay selection schemes are derived.

With the emergence of the Internet-of-Things (IoT), communication networks have evolved toward autonomous networks of intelligent devices capable of communicating without direct human intervention. This is known as Machine-to-Machine (M2M) communications. Cellular networks are considered one of the main technologies to support the deployment of M2M communications as they provide extended wireless connectivity and reliable communication links. However, the characteristics and Quality-of-Service (QoS) requirements of M2M communications are distinct from those of conventional cellular communications, also known as Human-to-Human (H2H) communications, that cellular networks were originally designed for. Thus, enabling M2M communications poses many challenges in terms of interference, congestion, spectrum scarcity and energy efficiency. The primary focus is on the problem of resource allocation that has been the interest of extensive research effort due to the fact that both M2M and H2H communications coexist in the cellular network. This requires that radio resources be allocated such that the QoS requirements of both groups are satisfied. In this work, we propose three models to address this problem. In the first model, a two-phase resource allocation algorithm for H2H/M2M coexistence in cellular networks is proposed. The goal is to meet the QoS requirements of H2H traffic and delay-sensitive M2M traffic while ensuring fairness for the delay-tolerant M2M traffic. Simulation results are presented which show that the proposed algorithm is able to balance the demands of M2M and H2H traffic, meet their diverse QoS requirements, and ensure fairness for delay-tolerant M2M traffic. With the growing number of Machine-Type Communication Devices (MTCDs) the problem of spectrum scarcity arises. Hence, Cognitive Radio (CR) is the focus of the second model where clustered Cognitive M2M (CM2M) communications underlaying cellular networks is proposed. In this model, MTCDs are grouped in clusters based on their spatial location and communicate with the Base Station (BS) via Machine-Type Communication Gateways (MTCGs). An underlay CR scheme is implemented where the MTCDs within a cluster share the spectrum of the neighbouring Cellular User Equipment (CUE). A joint resource-power allocation problem is formulated to maximize the sum-rate of the CUE and clustered MTCDs while adhering to MTCD minimum data rate requirements, MTCD transmit power limits, and CUE interference constraints. Simulation results are presented which show that the proposed scheme significantly improves the sum-rate of the network compared to other schemes while satisfying the constraints. Due to the limited battery capacity of MTCDs and diverse QoS requirements of both MTCDs and CUE, Energy Efficiency (EE) is critical to prolonging network lifetime to ensure uninterrupted and reliable data transmission. The third model investigates the power allocation problem for energy-efficient CM2M communications underlaying cellular networks. Underlay CR is employed to manage the coexistence of MTCDs and CUE and exploit spatial spectrum opportunities. Two power allocation problems are proposed where the first targets MTCD power consumption minimization while the second considers MTCD EE maximization subject to MTCD transmit power constraints, MTCD minimum data rate requirements, and CUE interference limits. Simulation results are presented which indicate that the proposed algorithms provide MTCD power allocation with lower power consumption and higher EE than the (Equal Power Allocation) EPA scheme while satisfying the constraints. / Graduate

abstract: Modern Communication systems are progressively moving towards all-digital transmitters (ADTs) due to their high efficiency and potentially large frequency range. While significant work has been done on individual blocks within the ADT, there are few to no full systems designs at this point in time. The goal of this work is to provide a set of multiple novel block architectures which will allow for greater cohesion between the various ADT blocks. Furthermore, the design of these architectures are expected to focus on the practicalities of system design, such as regulatory compliance, which here to date has largely been neglected by the academic community. Amongst these techniques are a novel upconverted phase modulation, polyphase harmonic cancellation, and process voltage and temperature (PVT) invariant Delta Sigma phase interpolation. It will be shown in this work that the implementation of the aforementioned architectures allows ADTs to be designed with state of the art size, power, and accuracy levels, all while maintaining PVT insensitivity. Due to the significant performance enhancement over previously published works, this work presents the first feasible ADT architecture suitable for widespread commercial deployment. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2019

Cognitive radio networks (CRN) have been tipped as one of the most promising paradigms for next generation wireless communication, due primarily to its huge promise of mitigating the spectrum scarcity challenge. To help achieve this promise, CRN develop mechanisms that permit spectrum spaces to be allocated to, and used by more than one user, either simultaneously or opportunistically, under certain preconditions. However, because of various limitations associated with CRN, spectrum and other resources available for use in CRN are usually very scarce. Developing appropriate models that can efficiently utilise the scarce resources in a manner that is fair, among its numerous and diverse users, is required in order to achieve the utmost for CRN. 'Resource allocation (RA) in CRN' describes how such models can be developed and analysed. In developing appropriate RA models for CRN, factors that can limit the realisation of optimal solutions have to be identified and addressed; otherwise, the promised improvement in spectrum/resource utilisation would be seriously undermined. In this thesis, by a careful examination of relevant literature, the most critical limitations to RA optimisation in CRN are identified and studied, and appropriate solution models that address such limitations are investigated and proffered. One such problem, identified as a potential limitation to achieving optimality in its RA solutions, is the problem of heterogeneity in CRN. Although it is indeed the more realistic consideration, introducing heterogeneity into RA in CRN exacerbates the complex nature of RA problems. In the study, three broad classifications of heterogeneity, applicable to CRN, are identified; heterogeneous networks, channels and users. RA models that incorporate these heterogeneous considerations are then developed and analysed. By studying their structures, the complex RA problems are smartly reformulated as integer linear programming problems and solved using classical optimisation. This smart move makes it possible to achieve optimality in the RA solutions for heterogeneous CRN. Another serious limitation to achieving optimality in RA for CRN is the strictness in the level of permissible interference to the primary users (PUs) due to the activities of the secondary users (SUs). To mitigate this problem, the concept of cooperative diversity is investigated and employed. In the cooperative model, the SUs, by assisting each other in relaying their data, reduce their level of interference to PUs significantly, thus achieving greater results in the RA solutions. Furthermore, an iterative-based heuristic is developed that solves the RA optimisation problem timeously and efficiently, thereby minimising network complexity. Although results obtained from the heuristic are only suboptimal, the gains in terms of reduction in computations and time make the idea worthwhile, especially when considering large networks. The final problem identified and addressed is the limiting effect of long waiting time (delay) on the RA and overall productivity of CRN. To address this problem, queueing theory is investigated and employed. The queueing model developed and analysed helps to improve both the blocking probability as well as the system throughput, thus achieving significant improvement in the RA solutions for CRN. Since RA is an essential pivot on which the CRN's productivity revolves, this thesis, by providing viable solutions to the most debilitating problems in RA for CRN, stands out as an indispensable contribution to helping CRN realise its much-proclaimed promises. / Thesis (PhD)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / PhD / Unrestricted