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Revenue-maximizing and truthful online auctions for dynamic spectrum access

机译:充收最大化和真实的在线拍卖,用于动态频谱访问

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Secondary spectrum auctions have been suggested as a strategically robust mechanism for distributing idle spectrum to competing secondary users. However, previous work on such auction design have assumed a static auction setting, thus failing to fully exploit the inherently time-varying nature of spectrum demand and utilization. In this paper, we address this issue from the perspective of the primary user who wishes to maximize the auction revenue. We present an online auction framework that dynamically accepts bids and allocates spectrum. We prove rigorously that our online auction framework is truthful in the multiple dimensions of bid values, as well as bid timing parameters. To protect against unbounded loss of revenue due to latter bids, we introduce controlled preemption into our mechanism. We prove that preemption, coupled with the technique of inflating bids artificially, leads to an online auction that guarantees a 1/5-fraction of the optimal revenue as obtained by an offline adversary. Since the previous guarantee holds only for the optimal channel allocation, we further provide a greedy channel allocation scheme which provides scalability. We prove that the greedy scheme also obtains a constant competitive revenue guarantee, where the constant depends on the parameter of the conflict graph.
机译:已经提出了二次频谱拍卖作为用于将空闲光谱分配给竞争辅助用户的策略性鲁棒机制。然而,之前的这种拍卖设计的工作已经假设了静态拍卖设置,因此未能充分利用频谱需求和利用的固有时变性。在本文中,我们从希望最大限度地提高拍卖收入的主要用户的角度来解决这个问题。我们提供了一个在线拍卖框架,动态接受出价并分配频谱。我们严格证明我们的在线拍卖框架在出价值的多个维度以及出价时序参数中是真实的。为了防止由于后者出价引起的无限收入损失,我们将控制的抢占引入我们的机制。我们证明了抢占,再加上膨胀投标的技术,导致在线拍卖,保证由离线对手获得的最佳收入的1/5分数。由于以前的保证仅适用于最佳信道分配,我们还提供了一种提供可扩展性的贪婪信道分配方案。我们证明,贪婪方案也获得了不断的竞争收入保证,其中常数取决于冲突图的参数。

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