In this paper, we present a new queueing model providing the accurate average system time for packets transmitted over a cognitive radio (CR) link for multiple traffic classes with the preemptive and non-preemptive priority service disciplines. The analysis considers general packet service time, general distributions for the channel availability periods and service interruption periods, and a service-resume transmission. We further introduce and analyze two novel priority service disciplines for opportunistic spectrum access (OSA) networks which take advantage of interruptions to preempt low priority traffic at a low cost. Analytical results, in addition to simulation results to validate their accuracy, are also provided and illustrate the impact of different OSA network parameters on the average system time. We particularly show that, for the same average CR transmission link availability, the packet system time significantly increases in a semi-static network with long operating and interruption periods compared to an OSA network with fast alternating operating and interruption periods. We also present results indicating that, due to the presence of interruptions, priority queueing service disciplines provide a greater differentiated service in OSA networks than in traditional networks. The analytical tools presented in this paper are general and can be used to analyze the traffic metrics of most OSA networks carrying multiple classes of traffic with priority queueing service differentiation.
@cite_11 we addressed several of those problems in a new queueing model for a single class of CR traffic for general operating and interruption period lengths. However, this model was limited to constant service time. @cite_18 , an optimal threshold for the queue length to decide whether a packet should join the queue or not is derived. However, the model is again not general and can not be used to analyze traffic metrics.
@cite_18 @cite_11
2059732808 2159182120 2136340918 2168965212
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. The main challenge to cognitive radio is the emergence of primary users, which can be considered as the service interruptions in a queuing system. The service interruption can incur significant delays for secondary users' data packets which are considered as secondary customers. Therefore, a secondary customer needs to decide whether to join the queue or leave for other means of transmission. It is shown that the individually optimal strategy for joining the queue is characterized by a threshold of queue length. When the current queue length is above this threshold, the secondary customer should leave; otherwise it should join the queue. The socially optimal threshold of queue length is also obtained and is numerically shown to be smaller than the individually optimal one, which implies that the individually optimal strategy does not yield the socially optimal one. To bridge the gap between the individually and socially optimal strategies, a pricing mechanism is proposed to toll the service of each secondary customer, thus equalizing the two optimal strategies. When the channel statistics are unknown, an online learning procedure, based on the Kiefer-Wolfowitz algorithm, is proposed. The proposed algorithms are then demonstrated using numerical simulations. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users. We consider a multiclass multiplexer with support for multiple service classes and dedicated buffers for each service class. Under specific scheduling policies for sharing bandwidth among these classes, we seek the asymptotic (as the buffer size goes to infinity) tail of the buffer overflow probability for each dedicated buffer. We assume dependent arrival and service processes as is usually the case in models of bursty traffic. In the standard large deviations methodology, we provide a lower and a matching (up to first degree in the exponent) upper bound on the buffer overflow probabilities. We introduce a novel optimal control approach to address these problems. In particular, we relate the lower bound derivation to a deterministic optimal control problem, which we explicitly solve. Optimal state trajectories of the control problem correspond to typical congestion scenarios. We explicitly and in detail characterize the most likely modes of overflow. We specialize our results to the generalized processor sharing policy (GPS) and the generalized longest queue first policy (GLQF). The performance of strict priority policies is obtained as a corollary. We compare the GPS and GLQF policies and conclude that GLQF achieves smaller overflow probabilities than GPS for all arrival and service processes for which our analysis holds. Our results have important implications for traffic management of high-speed networks and can be used as a basis for an admission control mechanism which guarantees a different loss probability for each class.