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Local large deviation principle, large deviation principle and information theory for the signal-to-interference-plus-noise ratio graph models

机译:局部大偏差原理,大偏差原理和信息理论的信号 - 干扰加噪声比图模型

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Given devices space D, an intensity measure lambda m epsilon (0, infinity), a transition kernel Q from the space D to positive real numbers R+, a path-loss function (which depends on the Euclidean distance between the devices and a positive constant alpha), we define a Marked Poisson Point process (MPPP). For a given MPPP and technical constants tau(lambda), gamma(lambda) : (0, infinity) - (0, infinity), we define a Marked Signal-to- Interference and Noise Ratio (SINR) graph, and associate with it two empirical measures; the empirical marked measure and the empirical connectivity measure.For a class of marked SINR graphs, we prove a joint large deviation principle(LDP) for these empirical measures, with speed lambda in the tau-topology. From the joint large deviation principle for the empirical marked measure and the empirical connectivity measure, we obtain an Asymptotic Equipartition Property(AEP) for network structured data modelled as a marked SINR graph. Specifically, we show that for large dense marked SINR graph one requires approximately about lambda H-2(Q x Q)/log 2 bits to transmit the information contained in the network with high probability, where H(Q x Q) is a properly defined entropy for the exponential transition kernel with parameter c.Further, we prove a local large deviation principle (LLDP) for the class of marked SINR graphs on D, where lambda[tau(lambda)(a)gamma(lambda) (a) + lambda tau(lambda)(b)gamma(lambda) (b)]-beta(a, b), a, b epsilon (0, infinity), with speed lambda from a spectral potential point. From the LLDP we derive a conditional LDP for the marked SINR graphs.Note that, while the joint LDP is established in the tau-topology, the LLDP assume no topological restriction on the space of marked SINR graphs. Observe also that all our rate functions are expressed in terms of the relative entropy or the kullback action or divergence function of the marked SINR on the devices space D.
机译:给定的设备空间D,强度测量Lambda M epsilon(0,Infinity),从空间D到正真数R +的转换内核Q,路径丢失函数(取决于器件之间的欧几里德距离和正常数) alpha),我们定义了标记的泊松点过程(MPPP)。对于给定的MPPP和技术常数Tau(Lambda),γ(Lambda):(0,Infinity) - & (0,Infinity),我们定义了标记的信号 - 干扰和噪声比(SINR)图,并将其与其联系起两个实证措施;对经验标记的措施和经验连接措施。对于一类标记的SINR图表,我们证明了这些实证措施的联合大偏差原理(LDP),在Tau-拓扑中的速度λ。从对经验标记措施和经验连接度量的联合大偏差原理,我们获得了作为标记SINR图形建模的网络结构化数据的渐近额度标准属性(AEP)。具体地,我们表明,对于大密集标记的SINR图形,需要大致λH-2(Q×Q)/ log 2位,以通过高概率传输网络中包含的信息,其中H(Q×Q)是正确的用参数c.further的指数转换内核定义了熵,我们证明了D的局部大偏差原理(LLDP),用于D的标记SINR图表,其中Lambda [Tau(Lambda)(a)γ(lambda)(a) +λTau(λ)(b)γ(λ)(b)] - &β(a,b),a,bε(0,无穷大),具有来自光谱潜在点的速度λ。从LLDP我们推导出标记的SINR图表的条件LDP.note,当在TAU拓扑中建立联合LDP时,LLDP在标记的SINR图表的空间上假设没有拓扑限制。另外观察,我们的所有速率函数都以具有设备空间D上标记的SINR的相对熵或扭矩函数或扭矩函数表示。

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