Reliability and Quality of Service in Opportunistic Spectrum Access

RÉSUMÉ Les réseaux radio-cognitif constituent une des meilleures options technologiques pour les réseaux sans-fil futurs. Afin d’étudier comment la fiabilité devrait être redéfinie dans ces réseaux, nous étudions d'abord les sources les plus fréquentes de panne dans les réseaux sans-fil et fournissons une procédure systématique de classement des pannes. Il est ensuite expliqué comment les radios cognitives peuvent profiter de leur propre capacité à mettre en œuvre des mécanismes efficaces de prévention et de récupération contre les pannes et ainsi assurer des communications sans-fil fiables et de qualité de service constante. En considérant des normes arrivantes sur la base de l'OSA, ce qui distingue un réseau radio-cognitif de ses prédécesseurs est des changements fréquents de canal ainsi que de nouvelles exigences telles la détection de disponibilité et la décision d'utilisation du spectre. Nous nous concentrons sur cet aspect et modélisons la remise du spectre comme une panne. Par conséquent, améliorer la fiabilité est équivalent à augmenter le temps moyen entre pannes, à rendre plus efficace le processus de récupération et à réduire le temps moyen de réparation. Nous étudions donc d'abord l'impact du temps de récupération sur la performance du réseau radio-cognitif. En classifiant les pannes en dures et souples, il est examiné comment la disponibilité, le temps moyen entre pannes et le temps moyen jusqu'à la réparation sont touchés par le procès de récupération. Nous observons que le temps dépensé pour la récupération empêche le réseau d'atteindre le maximum de disponibilité. Par conséquent, pour obtenir un temps plus élevé entre pannes et un temps de réparation plus court, une option disponible est d'augmenter le nombre de canaux pouvant être utilisés par le réseau radio-cognitif, de sorte que, avec une haute probabilité, un utilisateur qui a raté le canal puisse trouver bientôt un nouveau canal. De l'autre côté, un mécanisme de récupération efficace est nécessaire pour mieux profiter de ce grand nombre de canaux; l'amélioration de la récupération est donc indispensable. Pour étudier l'impact de la récupération sur les couches plus hautes (e.g., la couche liaison et réseau), l’approche de l’analyse de file d'attente est choisie. Compte tenu des périodes de récupération comme une interruption de service, un modèle général de file d'attente de M/G/1 avec des interruptions est proposé. Différents paramètres de fiabilité et de qualité de service peuvent être trouvés à partir de ce modèle de file d'attente pour étudier comment la spécification des canaux, tels la distribution des périodes de disponibilité et d'indisponibilité, et la spécification de l'algorithme de récupération, tels la durée de récupération, affectent les paramètres de performance comme la perte de paquets, de retard et de gigue, et aussi le temps entre pannes. Pour soutenir la différenciation des classes de trafic, nous proposons une approche de file d'attente avec priorité. Nous proposons une extension des résultats du modèle de file d'attente générale et présentons quatre différentes disciplines de file d'attente de priorité, allant d'un régime préemptif absolu à un régime complètement non préemptif. Les nouvelles disciplines augmentent la flexibilité et la résolution de décision et permettent au noeud CR de contrôler l'interaction des différentes classes de trafic avec plus de précision.---------- ABSTRACT Cognitive-radio based wireless networks are a technology of choice for incoming wireless networks. To investigate how reliability should be redefined for these networks, we study the most common sources of failure in wireless networks and provide a systematic failure classification procedure. It is then explained how cognitive radios can use their inherent capabilities to implement efficient prevention and recovery mechanisms to combat failures and thereby provide more reliable communications and consistent quality of service in wireless networks. Considering incoming OSA-based standards, what distinguishes a cognitive radio network from its predecessors is the frequent spectrum handovers along with new requirements such as spectrum sensing and spectrum usage decision. We thus focus on this aspect and model the spectrum handover as a failure, so improving the reliability is equivalent to increasing the mean time to failure, improving the recovery process and shortening the mean time to repair. We first study the impact of the recovery time on the performance of the cognitive radio network. By classifying the failures into hard and soft, it is investigated how the availability, mean time to failure and mean time to repair are affected by the recovery time. It is observed that the time spent for recovery prevents the network from reaching the maximum availability. Therefore, to achieve a high mean time to hard failure and low mean time to repair, an available option is to increase the number of channels, so that with a high probability, a user who missed the channel can soon find a new channel. On the other side, an efficient recovery scheme is required to better take advantage of a large number of channels. Recovery improvement is thus indispensable. To study the impact of recovery on higher communication layers, a queueing approach is chosen. Considering the recovery periods as a service interruption, a general M/G/1 queueing model with interruption is proposed. Different reliability and quality of service parameters can be found from this queueing model to investigate how channel parameters, such as availability and unavailability periods, and the recovery algorithm specifications, such as the recovery duration, affect packet loss, delay and jitter, and also the MTTF and MTTR for hard and soft failures. To support traffic differentiation, we suggest a priority queueing approach. We extend the results of the general queueing model and discuss four different priority queueing disciplines ranging from a pure preemptive scheme to a pure non-preemptive scheme. New disciplines increase the flexibility and decision resolution and enable the CR node to more accurately control the interaction of different classes of traffic. The models are solved, so it can be analyzed how the reliability and quality of service parameters, such as delay and jitter, for a specific class of traffic are affected not only by the channel parameters, but also by the characteristics of other traffic classes. The M/G/1 queueing model with interruptions is a foundation for performance analysis and an answer to the need of having closed-form analytical relations. We then extend the queueing model to more realistic scenarios, first with heterogeneous channels (heterogeneous service rate for different channels) and second with multiple users and a random medium access model

Channel Access and Reliability Performance in Cognitive Radio Networks:Modeling and Performance Analysis

Doktorgradsavhandling ved Institutt for Informasjons- og kommunikasjonsteknologi, Universitetet i AgderAccording to the facts and figures published by the international telecommunication union (ITU) regarding information and communication technology (ICT) industry, it is estimated that over 3.2 billion people have access to the Internet in 2015 [1]. Since 2000, this number has been octupled. Meanwhile, by the end of 2015, there were more than 7 billion mobile cellular subscriptions in the world, corresponding to a penetration rate of 97%. As the most dynamic segment in ICT, mobile communication is providing Internet services and consequently the mobile broadband penetration rate has reached 47% globally. Accordingly, capacity, throughput, reliability, service quality and resource availability of wireless services become essential factors for future mobile and wireless communications. Essentially, all these wireless technologies, standards, services and allocation policies rely on one common natural resource, i.e., radio spectrum. Radio spectrum spans over the electromagnetic frequencies between 3 kHz and 300 GHz. Existing radio spectrum access techniques are based on the fixed allocation of radio resources. These methods with fixed assigned bandwidth for exclusive usage of licensed users are often not efficient since most of the spectrum bands are under-utilized, either/both in the space domain or/and in the time domain. In reality, it is observed that many spectrum bands are largely un-occupied in many places [2], [3]. For instance, the spectrum bands which are exclusively allocated for TV broadcasting services in USA remain un-occupied from midnight to early morning according to the real-life measurement performed in [4]. In addition to the wastage of radio resources, spectrum under-utilization constraints spectrum availability for other intended users. Furthermore, legacy fixed spectrum allocation techniques are not capable of adapting to the changes and interactions in the system, leading to degraded network performance. Unlike in the static spectrum allocation, a fraction of the radio spectrum is allocated for open access as license-free bands, e.g., the industrial, scientific and medical (ISM) bands (902-928, 2400-2483.5, 5725-5850 MHz). In 1985, the federal communications commission (FCC) permitted to use the ISM bands for private and unlicensed occupancy, however, under certain restrictions on transmission power [5]. Consequently, standards like IEEE 802.11 for wireless local area networks (WLANs) and IEEE 802.15 for wireless personal area networks (WPAN) have grown rapidly with open access spectrum policies in the 2.4 GHz and 5 GHz ISM bands. With the co-existence of both similar and dissimilar radio technologies, 802.11 networks face challenges for providing satisfactory quality of service (QoS). This and the above mentioned spectrum under-utilization issues motivate the spectrum regulatory bodies to rethink about more flexible spectrum access for licenseexempt users or more efficient radio spectrum management. Cognitive radio (CR) is probably the most promising technology for achieving efficient spectrum utilization in future wireless networks