You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: 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.

[2]: 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.
[2] 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. [1] , 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.