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A modified GADIA-based upper-bound to the capacity of Gaussian general A/-relay networks

机译:基于改进的Gadia的高斯通用容量A / -Relay网络的容量

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摘要

In this paper, we present a general Gaussian N-relay network by allowing relays to communicate to each other and allowing a direct channel between source and destination as compared to the standard diamond network in Nazaroglu et al. (IEEE Trans Inf Theory 60:6329-6341, 2014) at the cost of extra channel uses. Our main focus is to examine the min-cut bound capacities of the relay network. Very recently, the results in Uykan (IEEE Trans Neural Netw Learn Syst 31:3294-3304, 2020) imply that the GADIA in Babadi and Tarokh (IEEE Trans Inf Theory 56:6228-6252, 2010), a pioneering algorithm in the interference avoidance literature, actually performs max-cut of a given power-domain (nonnegative) link gain matrix in the 2-channel case. Using the results of the diamond network in Nazaroglu et al. (2014) and the results in Uykan (2020), in this paper, we (i) turn the mutual information maximization problem in the Gaussian N-relay network into an upper bound minimization problem, (ii) propose a modified GADIA-based algorithm to find the min-cut capacity bound and (iii) present an upper and a lower bound to its min-cut capacity bound using the modified GADIA as applied to the defined "squared channel gain matrix/graph". Some advantages of the proposed modified GADIA-based simple algorithm are as follows: (1) The Gaussian N-relay network can determine the relay clusters in a distributed fashion and (2) the presented upper bound gives an insight into whether allowing the relays to communicate to each other pays off the extra channel uses or not as far as the min-cut capacity bound is concerned. The simulation results confirm the findings. Furthermore, the min-cut upper bound found by the proposed modified-GADIA is verified by the cut-set bounds found by the spectral clustering based solutions as well.
机译:在本文中,我们通过允许继电器彼此通信并允许源和目的地之间的直接信道相比,纳撒勒州的标准钻石网络相比,允许普通高斯N继电器网络。 (IEEE Trans Inf理论60:6329-6341,2014)以额外的渠道使用。我们的主要重点是检查继电器网络的闽切绑定容量。最近,uykan的结果(IEEE反式神经网络学习SYST 31:3294-3304,2020)暗示了巴比地和Tarokh(IEEE Trans Inf理论56:6228-6252,2010),干扰中的开创性算法避免文献,实际上在2通道案例中执行给定功率域(非负)链路增益矩阵的最大切割。使用纳拉古等人的钻石网络的结果。 (2014)和uykan(2020)的结果,在本文中,我们(i)将高斯N继电网网络中的互信息最大化问题转变为上限最小化问题,(ii)提出了一种基于Gadia的算法为了找到敏感的容量绑定和(iii)将上下绑定到下限和下限,其使用修改的Gadia绑定到所定义的“平方信道增益矩阵/图”。所提出的基于Gadia的简单算法的一些优点如下:(1)高斯N继电网网络可以以分布式方式确定中继簇,并且所提出的上限能够深入了解是否允许继电器允许继电器就闽割容量界限而言,互相互相兑换额外渠道使用。仿真结果证实了调查结果。此外,通过基于光谱聚类的解决方案发现的剪切界限验证了所提出的修饰Gadia的最小切割上限。

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