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Time-efficient distributed layer-2 auto-configuration for cognitive radio networks

机译:用于认知无线电网络的省时分布式第二层自动配置

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Cognitive radios (CR) have the ability to dynamically adapt to local spectrum availability. In a network comprised of CR-enabled devices, layer-2 auto-configuration involves determining a common set of channels to facilitate communication among participating nodes. This is a unique challenge as nodes in the CR network may be unaware of (a) their neighbors and (b) the channels on which they can communicate with a neighbor. In this paper, we propose a time-efficient distributed algorithm for layer-2 auto-configuration for a CR network. Our algorithm finds the globally common channel set in 2MN + O(DN) timeslots, where each node is assigned a unique identifier from the range [1,... ,N], M is the maximum number of channels available for communication, and D is the diameter of the network. All nodes know M and N. We present both diameter-aware and diameter-unaware versions of the algorithm. We then show that the proposed algorithms are efficient by proving a matching lower bound. Finally, we investigate a special case when nodes have more knowledge available at their disposal and discuss how the time-complexity of our algorithm can be improved under this case.
机译:认知无线电(CR)具有动态适应本地频谱可用性的能力。在由启用CR的设备组成的网络中,第2层自动配置涉及确定一组公共通道以促进参与节点之间的通信。这是一个独特的挑战,因为CR网络中的节点可能不知道(a)它们的邻居和(b)它们可以与邻居通信的通道。在本文中,我们为CR网络的第2层自动配置提出了一种高效的分布式算法。我们的算法找到了在2MN + O(DN)时隙中设置的全局公共信道,其中为每个节点分配了[1,...,N]范围内的唯一标识符,M是可用于通信的最大信道数,并且D是网络的直径。所有节点都知道M和N。我们介绍了该算法的直径感知和直径感知两种版本。然后,我们通过证明匹配的下界来证明所提出的算法是有效的。最后,我们研究了一种特殊情况,即当节点拥有更多可用知识时,并讨论在这种情况下如何提高算法的时间复杂性。

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