We provide a comprehensive delay and queueing analysis for two baseline medium access control protocols for multi-user cognitive radio networks with homogeneous users and channels and investigate the impact of different network parameters on the system performance. In addition to an accurate Markov chain, which follows the queue status of all users, several lower complexity queueing theory approximations are provided. Accuracy and performance of the proposed analytical approximations are verified with extensive simulations. It is observed that using an Aloha-type access to the control channel, a buffering MAC protocol, where in case of interruption the CR user waits for the primary user to vacate the channel before resuming the transmission, outperforms a switching MAC protocol, where the CR user vacates the channel in case of appearance of primary users and then compete again to gain access to a new channel. The reason is that the delay bottleneck for both protocols is the time required to successfully access the control channel, which occurs more frequently for the switching MAC protocol. It is thus shown that a clustering approach, where users are divided into clusters with a separate control channel per cluster, can significantly improve the performance by reducing the competitions over control channel.

Dynamic spectrum access (DSA) enables secondary networks to find and efficiently exploit spectrum opportunities. A key factor to design a DSA network is the spectrum sensing algorithms for multiple channels with multiple users. Multi-user cooperative channel sensing reduces the sensing time, and thus it increases transmission throughput. However, in a multi-channel system, the problem becomes more complex since the benefits of assigning users to sense channels in parallel must also be considered. A sensing schedule, indicating to each user the channel that it should sense at different sensing moments, must be thus created to optimize system performance. In this paper, we formulate the general sensing scheduling optimization problem and then propose several sensing strategies to schedule the users according to network parameters with homogeneous sensors. Later on, we extend the results to heterogeneous sensors and propose a robust scheduling design when we have traffic and channel uncertainty. We propose three sensing strategies, and, within each one of them, several solutions, striking a balance between throughput performance and computational complexity, are proposed. In addition, we show that a sequential channel sensing strategy is the one to be preferred when the sensing time is small, the number of channels is large, and the number of users is small. For all the other cases, a parallel channel sensing strategy is recommended in terms of throughput performance. We also show that a proposed hybrid sequential-parallel channel sensing strategy achieves the best performance in all scenarios at the cost of extra memory and computation complexity.

In this study, we propose a queueing model to analyse the performance of an opportunistic spectrum access (OSA) system with service interruptions operating over heterogeneous channels in which the service transmission rate and the service interruption rate after the transmission is resumed are generally different than their value prior to the interruption. We first propose Markov chain models to analyse this system under memoryless service time and availability periods. On the basis of simplification assumptions, we also provide an analytical z ‐Transform analysis of the Markov models. The Markov model and approximations are validated with accurate system simulations. We also provide numerical results illustrating the non‐convex relations between the traffic metrics and system parameters and that the proposed models are essential for optimal OSA network planning and operation. We further analyse and discuss the OSA queueing model for general distribution of service time and availability periods. The analytical and simulation results indicate that for usual system parameters, the queue average occupancy is similar for different distributions of service time and availability periods and that the memoryless Markov models can be used to accurately predict the heterogeneous OSA system traffic performance.

Dynamic spectrum access (DSA) for secondary networks improves the spectrum utilization by finding spectrum opportunities and exploiting them efficiently. A key factor to design a DSA network is the spectrum sensing algorithms for multiple channels with multiple users. Multi-user cooperative channel sensing reduces the sensing time, thus increasing the transmission throughput. However, in a multi-channel system, the problem becomes more complex since a sensing schedule, indicating to each user the channel that it must sense at different sensing moments, must be created to optimize system performance. In this paper, we first propose a general sensing strategy to schedule the users according to network parameters. We propose three sensing strategies, and within each one of them several solutions striking a balance between throughput performance, memory usage, and computational complexity are proposed. In addition, we show that the proposed sequential sensing strategy is the one to be preferred when the sensing time is small, the number of channels is large, and the number of users is small. For all the other cases, the parallel sensing strategy is recommended in terms of throughput performance. We also show that a proposed hybrid sequential-parallel sensing strategy achieves the best performance in all scenarios at the cost of extra complexity.

Cognitive radio networks (CRNs) benefit from several features, such as decision-making, spectrum-awareness and reconfigurability, which enable them to perform spectrum migration in order to recover the link when the operating channel becomes occupied. The time spent for channel migration and recovery is a function of the order in which the channels are selected to be sensed. For optimal sensing order, the parameters which have mostly been considered in the literature are the availability and the quality of the channels. Another important parameter is the channel stability, which is the duration that a channel remains continuously available. We extend in this paper a single-slot model to propose novel decision making dynamic programming (DP) models where availability, quality and stability of the channels are taken into account. Considering the need for differentiation in cognitive radio network, we also propose a differentiated dynamic programming model considering different classes of traffic where the sensing order is determined based on an aggregated cost function. Simulation results show the superiority of decision-making based on DP models compared to other common schemes such as myopic, random or average-based.

In this paper, we introduce a queueing approach with server interruptions to analyze the performance of a cognitive radio (CR) link subject to recurrent failures and interruptions. This general model can be used to analyze the traffic metrics as a function of different recovery schemes, channel bandwidth variations and service differentiation. As a first step, we solve in this paper the queueing model without service variation and differentiation. Numerical results to illustrate the analysis accuracy are provided.

In this paper, for the purpose of service differentiation in opportunistic spectrum access (OSA) enabled networks, we take different priority queueing disciplines into account as decision variables in a dynamic optimization model. The objective is to optimize, after the departure of each packet, a weighted utility function of the performance of different classes of cognitive radio traffic by dynamically deciding the priority queueing discipline. A dynamic programming (DP) model, which takes into account the channel availability and channel handover recovery periods of the OSA network, is provided for two classes of traffic to solve this problem. Simulation results are presented.

Cognitive radios (CR) are an efficient approach to deploy a secondary network in the vacant portions of the spectrum licensed to primary networks. However, when primary users return to a channel used by a CR, the later should vacate the channel and start a restoration process by searching and sensing other channels. The restoration process objective is to find the best channel in the shortest time. We propose in this paper a framework where the intrinsic features of learning and history-awareness of CRs are used to improve the restoration mechanism performance by providing a shorter restoration time or a restored channel with a higher quality. We study both distributed and centralized history-aware restoration schemes and show that it can provide significant improvements for both schemes. However, history-awareness should be carefully used in distributed algorithms since it can be detrimental in certain cases due to the increased users' contention for the best channels.