This paper considers an uplink massive machine-type communication (mMTC) scenario, where a large number of user devices are connected to a base station (BS). A novel grant-free massive random access (MRA) strategy is proposed, considering both the sporadic user traffic and short packet features. Specifically, the notions of active detection time (ADT) and active detection period (ADP) are introduced so that active user detection can be performed multiple times within one coherence time. By taking sporadic user traffic and short packet features into consideration, we model the joint active user detection and channel estimation issue into a dynamic compressive sensing (CS) problem with the underlying sparse signals exhibiting substantial temporal correlation. This paper builds a probabilistic model to capture the temporal structure and establishes a corresponding factor graph. A novel sequential approximate message passing (S-AMP) algorithm is designed to sequentially perform inference and recover sparse signal from one ADT to the next. The Bayes active user detector and the corresponding channel estimator are then derived. Numerical results show that the proposed S-AMP algorithm enhances active user detection and channel estimation performances over competing algorithms under our scenario.

中文翻译：

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具有零星短数据包的大规模随机接入：通过顺序消息传递进行联合活动用户检测和信道估计


本文考虑了上行链路大规模机器类型通信（mMTC）场景，其中大量用户设备连接到基站（BS）。考虑到零星的用户流量和短数据包的特点，提出了一种新颖的无授权大规模随机接入（MRA）策略。具体地，引入主动检测时间(ADT)和主动检测周期(ADP)的概念，使得在一个相干时间内可以多次执行主动用户检测。通过考虑零星用户流量和短数据包特征，我们将联合活动用户检测和信道估计问题建模为动态压缩感知（CS）问题，其中底层稀疏信号表现出显着的时间相关性。本文构建了一个概率模型来捕获时间结构并建立了相应的因子图。一种新颖的顺序近似消息传递 (S-AMP) 算法旨在顺序执行推理并恢复从一个 ADT 到下一个 ADT 的稀疏信号。然后导出贝叶斯活跃用户检测器和相应的信道估计器。数值结果表明，在我们的场景下，所提出的 S-AMP 算法比竞争算法增强了活动用户检测和信道估计性能。