Spectrum and transmission range aware clustering for cognitive radio ad hoc networks

Cognitive radio network (CRN) is a promising technology to overcome the problem of spectrum shortage by enabling the unlicensed users to access the underutilization spectrum bands in an opportunistic manner. On the other hand, the hardness of establishing a fixed infrastructure in specific situations such as disaster recovery, and battlefield communication imposes the network to have an ad hoc structure. Thus, the emerging of Cognitive Radio Ad Hoc Network (CRAHN) has accordingly become imperative. However, the practical implementation of CRAHN faced many challenges such as control channel establishment and the scalability problems. Clustering that divides the network into virtual groups is a reliable solution to handle these issues. However, previous clustering methods for CRAHNs seem to be impractical due to issues regarding the high number of constructed clusters and unfair load distribution among the clusters. Additionally, the homogeneous channel model was considered in the previous work despite channel heterogeneity is the CRN features. This thesis addressed these issues by proposing two clustering schemes, where the heterogeneous channel is considered in the clustering process. First, a distributed clustering algorithm called Spectrum and Transmission Range Aware Clustering (STRAC) which exploits the heterogeneous channel concept is proposed. Here, a novel cluster head selection function is formulated. An analytical model is derived to validate the STRAC outcomes. Second, in order to improve the bandwidth utilization, a Load Balanced Spectrum and Transmission Range Aware Clustering (LB-STRAC) is proposed. This algorithm jointly considers the channel heterogeneity and load balancing concepts. Simulation results show that on average, STRAC reduces the number of constructed clusters up to 51% compared to conventional clustering technique, Spectrum Opportunity based Clustering (SOC). In addition, STRAC significantly reduces the one-member cluster ratio and re-affiliation ratio in comparison to non-heterogeneity channel consideration schemes. LB-STRAC further improved the clustering performance by outperforming STRAC in terms of uniformity and equality of the traffic load distribution among all clusters with fair spectrum allocation. Moreover, LB-STRAC has been shown to be very effective in improving the bandwidth utilization. For equal traffic load scenario, LB-STRAC on average improves the bandwidth utilization by 24.3% compared to STRAC. Additionally, for varied traffic load scenario, LB-STRAC improves the bandwidth utilization by 31.9% and 25.4% on average compared with STRAC for non-uniform slot allocation and for uniform slot allocation respectively. Thus, LB-STRAC is highly recommended for multi-source scenarios such as continuous monitoring applications or situation awareness applications

Spectrum Allocation Algorithms for Cognitive Radio Mesh Networks

Empowered by the cognitive radio technology, and motivated by the sporadic channel utilization, both spatially and temporally, dynamic spectrum access networks (also referred to as cognitive radio networks and next generation wireless networks) have emerged as a solution to improve spectrum utilization and provide more flexibility to wireless communication. A cognitive radio network is composed of wireless users, referred to as secondary users, which are allowed to use licensed spectrum bands as long as their are no primary, licensed, users occupying the channel in their vicinity. This restricted spectrum access strategy leads to heterogeneity in channel availability among secondary users. This heterogeneity forms a significant source of performance degradation for cognitive radio networks, and poses a great challenge on protocol design. In this dissertation, we propose spectrum allocation algorithms that take into consideration the heterogeneity property and its effect on the network performance. The spectrum allocation solutions proposed in this dissertation address three major objectives in cognitive radio mesh networks. The first objective is maximizing the network coverage, in terms of the total number of served clients, and at the same time simplifying the communication coordination function. To address this objective, we proposed a received based channel allocation strategy that alleviates the need for a common control channel, thus simplifying the coordination function, and at the same time maximizes the number of clients served with link reliability guarantees. We show the superiority of the proposed allocation strategy over other existing strategies. The second objective is improving the multicast throughput to compensate for the performance degradation caused by channel heterogeneity. We proposed a scheduling algorithm that schedules multicast transmissions over both time and frequency and integrates that with the use of network coding. This algorithm achieves a significant gain, measured as the reduction in the total multicast time, as the simulation results prove. We also proposed a failure recovery algorithm that can adaptively adjust the schedule in response to temporary changes in channel availability. The last objective is minimizing the effect of channel switching on the end-to-end delay and network throughput. Channel switching can be a significant source of delay and bandwidth wastage, especially if the secondary users are utilizing a wide spectrum band. To address this issue, we proposed an on-demand multicast routing algorithm for cognitive radio mesh networks based on dynamic programming. The algorithm finds the best available route in terms of end-to-end delay, taking into consideration the switching latency at individual nodes and the transmission time on different channels. We also presented the extensibility of the proposed algorithm to different routing metric. Furthermore, a route recovery algorithm that takes into consideration the overhead of rerouting and the route cost was also proposed. The gain of these algorithms was proved by simulation

Performance of MIMO Cognitive Ad-hoc Networks

Cognitive ad-hoc networks are able to share primary user frequency bands following certain interference preconditions. For considered cognitive network, cognitive communication is limited by the interference imposed on the primary user. Probability of channel availability for cognitive nodes for such opportunistic access is determined. Furthermore, this probability of channel availability is used for the performance analysis purpose. A Carrier Sense Multiple Access (CSMA) Media Access Control (MAC) protocol for the cognitive network is considered and for that the embedded Markov model of cognitive nodes is determined. This Markov model is used to determine the average channel access delay, throughput and service rate of cognitive nodes. This network is further extended to consider multiple frequency bands for cognitive access. For this propose algorithms are proposed to address the channel allocation and fairness issues of multi-band multiuser cognitive ad-hoc networks. Nodes in the network have unequal channel access probability and have no prior information about the offered bandwidth or number of users in the multiple access system. In that, nodes use reinforcement learning algorithm to predict future channel selection probability from the past experience and reach an equilibrium state. Proof of convergence of this multi party stochastic game is established. Nevertheless, cognitive nodes can reduce the convergence time by exchanging channel selection information and thus further improve the network performance. To further improve the spectrum utilization, this study is extended to include Multiple-input Multiple-output (MIMO) techniques. To improve the transmission efficiency of the MIMO system, a cross-layer antenna selection algorithm is proposed. The proposed cross-layer antenna selection and beamforming algorithm works as the data link layer efficiency information is used for antenna selection purpose to achieve high efficiency at the data link layer. Having analyzed the cognitive network, to consider more realistic scenario primary users identification method is proposed. An artificial intelligent method has been adopted for this purpose. Numerical results are presented for the algorithm and compare these results with the theoretical ones

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