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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 detectionperiod (ADP) are introduced so that active user detection can be performedmultiple times within one coherence time. By taking sporadic user traffic andshort packet features into consideration, we model the joint active userdetection and channel estimation issue into a dynamic compressive sensing (CS)problem with the underlying sparse signals exhibiting substantial temporalcorrelation. This paper builds a probabilistic model to capture the temporalstructure and establishes a corresponding factor graph. A novel sequentialapproximate message passing (S-AMP) algorithm is designed to sequentiallyperform inference and recover sparse signal from one ADT to the next. The Bayesactive user detector and the corresponding channel estimator are then derived.Numerical results show that the proposed S-AMP algorithm enhances active userdetection and channel estimation performances over competing algorithms underour scenario.

Massive Random Access with Sporadic Short Packets: Joint Active User Detection and Channel Estimation via Sequential Message Passing