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In this work, we present a joint channel estimation, activity detection anddata decoding scheme for massive machine-type communications. By including thechannel and the a priori activity factor in the factor graph, we present thebilinear message-scheduling GAMP (BiMSGAMP), a message-passing solution thatuses the channel decoder beliefs to refine the activity detection and datadecoding. We include two message-scheduling strategies based on the residualbelief propagation and the activity user detection in which messages areevaluated and scheduled in every new iteration. An analysis of the convergenceof BiMSGAMP along with a study of its computational complexity is carried out.Numerical results show that BiMSGAMP outperforms state-of-the-art algorithms,highlighting the gains achieved by using the dynamic scheduling strategies andthe effects of the channel decoding part in the system.

Joint Channel Estimation, Activity Detection and Decoding using Dynamic Message-Scheduling for Machine-Type Communications