In this thesis, we investigate the impact of channels that have a bursty nature in a cognitive radio network scenario. Our goal is to design a general channel usage model that can handle bursty primary user channel usage. The proposed model describes idle periods with a discrete platoon arrival process and describes busy periods with a discrete phase type distribution. The performance of the proposed model is compared with two more traditionally encountered channel usage models in three different secondary user access schemes. First, we design a reactive access scheme to show the poor performance results an in- vestigator can potentially obtain when ignoring bursty data traffic. We have also analyzed two proactive secondary network access schemes. Numerical results show that the achiev- able utilization and interference probability of the network are affected when traditional channel models are used in a bursty PU channel.

In this thesis, we investigate the impact of channels that have a bursty nature in a cognitive radio network scenario. Our goal is to design a general channel usage model that can handle bursty primary user channel usage. The proposed model describes idle periods with a discrete platoon arrival process and describes busy periods with a discrete phase type distribution. The performance of the proposed model is compared with two more traditionally encountered channel usage models in three different secondary user access schemes. First, we design a reactive access scheme to show the poor performance results an in- vestigator can potentially obtain when ignoring bursty data traffic. We have also analyzed two proactive secondary network access schemes. Numerical results show that the achiev- able utilization and interference probability of the network are affected when traditional channel models are used in a bursty PU channel.

In the expanding spectrum marketplace, there has been a long term evolution towards more market€“oriented mechanisms, such as Opportunistic Spectrum Access (OSA), enabled through Cognitive Radio (CR) technology. However, the potential of CR technologies to revolutionize wireless communications, also introduces challenges based upon the potentially non€“deterministic CR behaviour in the Electrospace. While establishing and enforcing compliance to spectrum etiquette rules are essential to realization of successful OSA networks in the future, there has only been recent increased research activity into enforcement. This dissertation presents novel work on the spectrum monitoring aspect, which is crucial to effective enforcement of OSA. An overview of the challenges faced by current compliance monitoring methods is first presented. A framework is then proposed for the use of random spectral sampling techniques to reduce data collection complexity in wideband sensing scenarios. This approach is recommended as an alternative to Compressed Sensing (CS) techniques for wideband spectral occupancy estimation, which may be difficult to utilize in many practical congested scenarios where compliance monitoring is required. Next, a low€“cost computational approach to online randomized temporal sensing deployment is presented for characterization of temporal spectrum occupancy in cognitive radio scenarios. The random sensing approach is demonstrated and its performance is compared to CS€“based approach for occupancy estimation. A novel frame€“based sampling inversion technique is then presented for cases when it is necessary to track the temporal behaviour of individual CRs or CR networks. Parameters from randomly sampled Physical Layer Convergence Protocol (PLCP) data frames are used to reconstruct occupancy statistics, taking account of missed frames due to sampling design, sensor limitations and frame errors. Finally, investigations into the use of distributed and mobile spectrum sensing to collect spatial diversity to improve the above techniques are presented, for several common monitoring tasks in spectrum enforcement. Specifically, focus is upon techniques for achieving consensus in dynamic topologies such as in mobile sensing scenarios.

Recently, with the fast development of wireless communications, the radio spectrum becomes a precious natural resource. Many researches and reports reveal the problems of inefficient spectrum utilization. Cognitive Radio (CR) technology is now developing for solving this critical problem. This technology will enable various kinds of wireless systems to look for and connect radio frequency spectrum that the locality leave unused by oneself, to offer the best service to user. The CR will pass in and out the idle frequency band according to the demand while receiving and dispatching the signal, avoid the frequency band that has been already used. In CR network, the objective is to maximize the throughput of secondary users while limiting the probability of colliding with primary users below a prescribed level. In this paper, we consider a distributed secondary networks model where users seek spectrum opportunities independently that overlaying the primary networks to analyze the system performance and the effect to the primary users with the existence of both primary users and secondary users under the cognitive radio networks. In the cognitive system, due to the existence of noise and fading effect, error detection cannot be avoided. Therefore, we made a comparison to the difference of the efficiency among environments of different probability of miss detection. We also propose a random hopping method for all secondary users in system will re-sensing after a random period of time. Hereby, efficiently decreases the ratio of time that influences the primary users by the secondary users, and further research the factor that influences its efficiency.

Opportunistic spectrum access (OSA) promises to significantly improve the utilization of the RF spectrum. Under OSA, an unlicensed secondary user (SU) is allowed to detect and access under-utilized portions of the licensed spectrum, provided that such operation does not interfere with the communication of licensed primary users (PUs). Cognitive radio (CR) is a key enabling technology of OSA. In this dissertation, we propose several radio resource management and routing mechanisms that optimize the discovery and utilization of spectrum opportunities in a cognitive radio network (CRN). First, we propose a sequential channel sensing and probing mechanism that enables a resource-constrained SU to efficiently identify the optimal transmission opportunity from a pool of potentially usable channels. This mechanism maximizes the SUs expected throughput by accounting for the tradeoff between the reward and overhead of scanning additional channels. The optimal channel sensing and probing process is modeled as a maximum-rate-of-return problem in optimal stopping theory. Operational parameters, such as sensing and probing times, are optimized by exploiting the problem's special structure. Second, we study the problem of coordinated spectrum access in CRNs to maximize the CRNs throughput. By exploiting the geographic relationship between an SU and its surrounding PUs, we propose the novel concept of microscopic spectrum opportunity, in which active SUs and PUs are allowed to operate in the same region, subject to power constraints. Under this framework, we formulate the coordinated channel access problem as a joint power/rate control and channel assignment optimization problem. Centralized and distributed approximate algorithms are proposed to solve this problem efficiently. Compared with its macroscopic counterpart, we show that the microscopic-spectrum-opportunity framework offers significant throughput gains. Finally, at the network layer, we study the problem of truthful least-priced-path (LPP) routing for profit-driven CRNs. We design a route selection and pricing mechanism that guarantees truthful spectrum cost reporting from profit-driven SUs and that finds the cheapest route for end users. The problem is investigated with and without capacity constraints at individual nodes. In both cases, polynomial-time algorithms are developed to solve the LPP problem. Extensive simulations are conducted to verify the validity of the proposed mechanisms.

This report describes my M.A.Sc. thesis research work. The emerging 4th generation (4G) mobile systems and networks (so called 4G HetNets) are designed as multilayered cellular topology with a number of asymmetrically located, asymmetrically powered, self-organizing, and user-operated indoor small cell (e.g., pico/femto cells and WLANs) with a variety of cell architectures that are overlaid by a large cell (macro cell) with some or all interfering wireless links. These designs of 4G HetNets bring new challenges such as increased dynamics of user mobility and data traffic trespassing over the multi-layered cell boundaries. Traditional approaches of radio resource allocation and inter-cell (cochannel) interference management that are mostly centralized and static in the network core and are carried out pre-hand by the operator in 3G and lower cellular technologies, are liable to increased signaling overhead, latencies, complexities, and scalability issues and, thus, are not viable in case of 4G HetNets. In this thesis a comprehensive research study is carried out on improving the radio resource sharing and inter-cell interference management in 4G HetNets. The solution strategy exploits dynamic and adaptive channel allocation approaches such as dynamic and opportunistic spectrum access (DSA, OSA) techniques, through exploiting the spatiotemporal diversities among transmissions in orthogonal frequency division multiple access (OFDMA) based medium access in 4G HetNets. In this regards, a novel framework named as Hybrid Radio Resource Sharing (HRRS) is introduced. HRRS comprises of these two functional modules: Cognitive Radio Resource Sharing (CRRS) and Proactive Link Adaptation (PLA) scheme. A dynamic switching algorithm enables CRRS and PLA modules to adaptively invoke according to whether orthogonal channelization is to be carried out exploiting the interweave channel allocation (ICA) approach or non-orthogonal channelization is to be carried out exploiting the underlay channel allocation (UCA) approach respectively when relevant conditions regarding the traffic demand and radio resource availability are met. Benefits of CRRS scheme are identified through simulative analysis in comparison to the legacy cochannel and dedicated channel deployments of femto cells respectively. The case study and numerical analysis for PLA scheme is carried out to understand the dynamics of threshold interference ranges as function of transmit powers of MBS and FBS, relative ranges of radio entities, and QoS requirement of services with the value realization of PLA scheme.

The radio frequency spectrum is not being efficiently utilized partly due to the current policy of allocating the frequency bands to specific services and users. In opportunistic spectrum access (OSA), the ``white spaces'' that are not occupied by primary users (a.k.a. incumbent users) can be opportunistically utilized by secondary users. To achieve this, we need to solve two problems: (i) primary-secondary incumbent protection, i.e., prevention of harmful interference from secondary users to primary users; (ii) secondary-secondary network coexistence, i.e., mitigation of mutual interference among secondary users. The first problem has been addressed by spectrum sensing techniques in cognitive radio (CR) networks and geolocation database services in database-driven spectrum sharing. The second problem is the main focus of this dissertation. To obtain a clear picture of coexistence issues, we propose a taxonomy of heterogeneous coexistence mechanisms for shared spectrum access. Based on the taxonomy, we choose to focus on four typical coexistence scenarios in this dissertation. Firstly, we study sensing-based OSA, when secondary users are capable of employing the channel aggregation technique. However, channel aggregation is not always beneficial due to dynamic spectrum availability and limited radio capability. We propose a channel usage model to analyze the impact of both primary and secondary user behaviors on the efficiency of channel aggregation. Our simulation results show that user demands in both the frequency and time domains should be carefully chosen to minimize expected cumulative delay. Secondly, we study the coexistence of homogeneous CR networks, termed as self-coexistence, when co-channel networks do not rely on inter-network coordination. We propose an uplink soft frequency reuse technique to enable globally power-efficient and locally fair spectrum sharing. We frame the self-coexistence problem as a non-cooperative game, and design a local heuristic algorithm that achieves the Nash equilibrium in a distributed manner. Our simulation results show that the proposed technique is mostly near-optimal and improves self-coexistence in spectrum utilization, power consumption, and intra-cell fairness. Thirdly, we study the coexistence of heterogeneous CR networks, when co-channel networks use different air interface standards. We propose a credit-token-based spectrum etiquette framework that enables spectrum sharing via inter-network coordination. Specifically, we propose a game-auction coexistence framework, and prove that the framework is stable. Our simulation results show that the proposed framework always converges to a near-optimal distributed solution and improves coexistence fairness and spectrum utilization. Fourthly, we study database-driven OSA, when secondary users are mobile. The use of geolocation databases is inadequate in supporting location-aided spectrum sharing if the users are mobile. We propose a probabilistic coexistence framework that supports mobile users by locally adapting their location uncertainty levels in order to find an appropriate trade-off between interference mitigation effectiveness and location update cost. Our simulation results show that the proposed framework can determine and adapt the database query intervals of mobile users to achieve near-optimal interference mitigation with minimal location updates. / Ph. D.

No description available.

Radio spectrum has become increasingly scarce due to the proliferation of new wireless communication services. This problem has been exacerbated by fixed bandwidth licensing policies that often lead to spectral underutilization. Cognitive radio networks (CRN) can address this issue using flexible spectrum management that permits unlicensed (secondary) users to access the licensed spectrum. Supporting real-time quality-of-service (QoS) in CRNs however, is very challenging, due to the random spectrum availability induced by the licensed (primary) user activity. This thesis considers the problem of real-time voice transmission in CRNs with an emphasis on secondary network ``friendliness''. Friendliness is measured by the secondary real-time voice capacity, defined as the number of connections that can be supported, subject to typical QoS constraints. The constant bit rate (CBR) air interface case is first assumed. An offline scheduler that maximizes friendliness is derived using an integer linear program (ILP) that can be solved using a minimum cost flow graph construction. Two online primary scheduling algorithms are then introduced. The first algorithm is based on shaping the primary spectral hole patterns subject to primary QoS constraints. The second applies real-time scheduling to both primary traffic and virtual secondary calls. The online scheduling algorithms are found to perform well compared to the friendliness upper bound. Extensive simulations of the primary friendly schedulers show the achievable secondary voice capacity for a variety of parameters compared to non-friendly primary scheduling. The thesis then considers the variable bit rate (VBR) air interface option for primary transmissions. Offline and online approaches are taken to generate a primary VBR traffic schedule that is friendly to secondary voice calls. The online VBR schedulers are found to perform well compared to the friendliness upper bound. Simulation results are presented that show the effect of the primary traffic load and primary network delay tolerance on the primary network friendliness level towards potential secondary voice traffic. Finally, secondary user friendliness is considered from an infrastructure deployment point of view. A cooperative framework is proposed, which allows the primary traffic to be relayed by helper nodes using decode-and-forward (DF) relaying. This approach decreases the primary traffic channel utilization, which, in turn, increases the capacity available to potential secondary users. A relay selection optimization problem is first formulated that minimizes the primary channel utilization. A greedy algorithm that assigns relay nodes to primary data flows is introduced and found to perform well compared to the optimum bound. Results are presented that show the primary network friendliness for different levels of primary channel utilization. / Dissertation / Doctor of Philosophy (PhD)

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.