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arXiv 2006.05038cs.NIcs.LGmath.PR
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确定性调度下无线网络的覆盖概率

Coverage probability in wireless networks with determinantal scheduling

Inria(法国国家信息与自动化研究所);ENS(巴黎高等师范学院);Huawei(华为);University of Melbourne(墨尔本大学);ACEMS(澳大利亚数学与统计前沿中心)

Bartek Błaszczyszyn, Antoine Brochard, H. Paul Keeler

摘要:

我们提出了一类新的算法随机调度网络传输。其思想是使用(离散)决定点过程(子集)来随机地将介质访问分配给潜在发射机的各种{ \em排斥}子集。这种方法可以被视为(空间)Aloha的自然扩展,它独立地调度传输。根据一般的路径损耗模型和瑞利衰落,我们表明,类似于Aloha,他们也受到优雅的分析的覆盖概率和传输尝试(也称为本地延迟)。这主要是由于明确的,决定性的形式的条件(棕榈)分布和封闭形式的表达式拉普拉斯功能的决定性过程。有趣的是,所得到的网络的性能特性是服从各种优化的调度参数,这是决定性的内核,允许使用的技术开发的统计学习与决定性的过程。确定性过程的成熟采样算法可以用来处理实现问题,这超出了本文的范围,但它为进一步的研究创造了路径。

英文摘要:

We propose a new class of algorithms for randomly scheduling network transmissions. The idea is to use (discrete) determinantal point processes (subsets) to randomly assign medium access to various {\em repulsive} subsets of potential transmitters. This approach can be seen as a natural extension of (spatial) Aloha, which schedules transmissions independently. Under a general path loss model and Rayleigh fading, we show that, similarly to Aloha, they are also subject to elegant analysis of the coverage probabilities and transmission attempts (also known as local delay). This is mainly due to the explicit, determinantal form of the conditional (Palm) distribution and closed-form expressions for the Laplace functional of determinantal processes. Interestingly, the derived performance characteristics of the network are amenable to various optimizations of the scheduling parameters, which are determinantal kernels, allowing the use of techniques developed for statistical learning with determinantal processes. Well-established sampling algorithms for determinantal processes can be used to cope with implementation issues, which is is beyond the scope of this paper, but it creates paths for further research.

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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