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

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