大规模随机接入中信息新鲜度的时空模型
A Spatio-Temporal Model for Information Freshness in Massive Random Access
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中文总结 AI 辅助
针对5G大规模IoT设备随机接入场景，提出融合时空维度的信息新鲜度模型，推导不同接收器性能并优化节点传输概率以最小化预期不确定性。
中文摘要 AI 辅助
大规模连接是5G的关键组成部分，有望在下一代无线系统中发挥重要作用，且通过对与海量物联网（IoT）设备相关的信息动态建模，其预期需求正在发生变革。受此启发，本文提出一种模型，该模型通过简单标量参数（即成功概率和接收更新的准确性），捕获从极大量IoT设备通过随机接入信道策略发送的信息新鲜度的时空特性。存在诸多信息新鲜度度量指标，从信息年龄（AoI）开始，所有这些指标都是在时间维度上表征的实际应用性能的代理。我们的模型在此基础上增加了空间维度，注意到分布在同一区域的传感器可能存在强相关性，来自多个邻近传感器的信息可提高接收器的整体准确性。我们聚焦于表征接收器的不确定性，该不确定性通过条件熵表达，考虑一个由部分可靠、空间分布的传感器组成的网络，这些传感器观测同一过程并通过时隙ALOHA信道报告其测量值。我们考虑一个简单的遗忘型接收器和一个更完整的模型（该模型考虑过去观测的完整历史），推导它们的性能，并优化节点的传输概率以最小化预期不确定性。
英文摘要
Massive connectivity, a key building block of 5G, is expected to play an important role in the next generation of wireless systems, and its expected requirements are being revolutionized through the modeling of the information dynamics related to the vast numbers of Internet of things (IoT) devices. Motivated by this, the present paper introduces a model that captures the spatio-temporal nature of freshness of information sent via random access channel policies from an extremely large set of IoT devices via simple scalar parameters, i.e., the probability of success and accuracy of received updates. There are many information freshness metrics, starting from the age of information (AoI), all of which are proxies for the actual application performance, characterized over the temporal dimension. Our model adds the spatial dimension to this picture, observing that sensors distributed over the same area may have a strong correlation, and information from multiple close-by sensors may improve the overall accuracy of the receiver. We focus on characterizing the uncertainty of the receiver, expressed through the conditional entropy, considering a network of partially reliable, spatially distributed sensors observing the same process and reporting their measurements over a slotted ALOHA channel. We consider a simple forgetful receiver and a more complete model which accounts for the full history of past observations, deriving their performance, and optimizing the transmission probability of nodes to minimize the expected uncertainty.
