arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1407.7150cs.ITmath.ITstat.AP
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基于编码理论的树形网络分布式推理

Distributed Inference in Tree Networks using Coding Theory

Bhavya Kailkhura, Aditya Vempaty, Pramod K. Varshney

摘要:

在本文中,我们考虑的问题,分布式推理树为基础的网络。在本文所考虑的框架中,分布式节点在通过中间结点将其发送到融合中心(FC)之前,对一种现象进行1位本地决策。我们建议使用基于编码理论的技术来解决这种结构中的分布式推理问题。数据在向FC移动时会逐渐压缩。FC在接收到来自中间结点的数据后进行全局推理。通过纠错码实现节点处以及FC处的数据融合。在这种情况下,我们分析了一个给定的代码矩阵的性能,也设计了最佳的代码矩阵在每一个层次的树。我们分别解决分布式分类和分布式估计的问题,并制定计划,在树网络中执行这些任务。所提出的方案结构简单,具有实际意义。我们研究了两种不同类型的树网络:固定高度的树网络,固定度的树网络,我们的计划的渐近推理性能。我们证明了在一定条件下,所提出的方案是渐近最优的。

英文摘要:

In this paper, we consider the problem of distributed inference in tree based networks. In the framework considered in this paper, distributed nodes make a 1-bit local decision regarding a phenomenon before sending it to the fusion center (FC) via intermediate nodes. We propose the use of coding theory based techniques to solve this distributed inference problem in such structures. Data is progressively compressed as it moves towards the FC. The FC makes the global inference after receiving data from intermediate nodes. Data fusion at nodes as well as at the FC is implemented via error correcting codes. In this context, we analyze the performance for a given code matrix and also design the optimal code matrices at every level of the tree. We address the problems of distributed classification and distributed estimation separately and develop schemes to perform these tasks in tree networks. The proposed schemes are of practical significance due to their simple structure. We study the asymptotic inference performance of our schemes for two different classes of tree networks: fixed height tree networks, and fixed degree tree networks. We show that the proposed schemes are asymptotically optimal under certain conditions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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