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Socially Optimal Queuing Control in Cognitive Radio Networks Subject to Service Interruptions: To Queue or Not to Queue?

机译:服务中断下认知无线电网络中的社会最优排队控制:要排队还是不排队?

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摘要

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.
机译:认知无线电的主要挑战是主要用户的出现,可以将其视为排队系统中的服务中断。服务中断可能会导致被视为次要客户的次要用户数据包的大量延迟。因此,次要客户需要决定是加入队列还是离开其他传输方式。结果表明,加入队列的个体最优策略的特征在于队列长度的阈值。当前队列长度超过此阈值时,次要客户应离开;否则,它应该加入队列。还获得了队列长度的社会最优阈值,并在数值上显示为小于个体最优阈值,这意味着个体最优策略不会产生社会最优策略。为了弥合个体最优策略和社会最优策略之间的差距,提出了一种定价机制以向每个次要客户收费,从而使这两种最优策略均等。当信道统计信息未知时,提出了一种基于Kiefer-Wolfowitz算法的在线学习过程。然后使用数值模拟来证明所提出的算法。

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