The article obtains large deviation asymptotic for sub-critical communication networks modelled as signal-interference-noise-ratio(SINR) random networks. To achieve this, we define the empirical power measure and the empirical connectivity measure, as well as prove joint large deviation principles(LDPs) for the two empirical measures on two different scales. Using the joint LDPs, we prove an Asymptotic equipartition property(AEP) for wireless telecommunication Networks modelled as the subcritical SINR random networks. Further, we prove a Local Large deviation principle(LLDP) for the sub-critical SINR random network. From the LLDPs, we prove the large deviation principle, and a classical McMillan Theorem for the stochastic SINR model processes. Note that, the LDPs for the empirical measures of this stochastic SINR random network model were derived on spaces of measures equipped with the $\tau-$ topology, and the LLDPs were deduced in the space of SINR model process without any topological limitations. We motivate the study by describing a possible anomaly detection test for SINR random networks.

翻译：为实现这一目标,我们界定了实证权力量度和实证连通度,并证明两种不同尺度的实验性衡量方法的共同大偏差原则。我们利用联合LDP,证明对模拟为次临界SINR随机网络的无线电信网络来说,我们是一个无线通信网络的Astyteminative设备属性(AEP)。此外,我们证明对次临界SINR随机网络来说,当地大偏差原则(LLDP)是当地大型偏差原则(LLLDP),从LLDP中,我们证明大偏差原则,以及典型的McMillan理论为SIRR模型过程的随机偏差原则(LDP)。我们指出,这种随机网络模型的实验性测量方法是在配备了$tau-$表理学的测量空间上得出的,而LLDP是在SINR模型进程中推算出来的,没有任何顶尖学限制。我们通过描述可能为SINR随机网络进行的异常现象检测测试来激发研究。