  In this work, based on the hybrid generalized approximate message passing
(HyGAMP) algorithm, we propose the message-scheduling GAMP (MSGAMP) algorithm
in order to address the problem of joint active device detection and channel
estimation in an uplink grant-free massive MIMO system scenario. In MSGAMP, we
apply three different scheduling techniques based on the Residual Belief
Propagation (RBP) in which messages are generated using the latest available
information. With a much lower computational cost than the state-of-the-art
algorithms, MSGAMP-type schemes exhibits good performance in terms of activity
error rate and normalized mean squared error, requiring a small number of
iterations for convergence. %
