  Massive access has been challenging for the fifth generation (5G) and beyond
since the abundance of devices causes communication overload to skyrocket. In
an uplink massive access scenario, device traffic is sporadic in any given
coherence time. Thus, channels across the antennas of each device exhibit
correlation, which can be characterized by the row sparse channel matrix
structure. In this work, we develop a bilinear generalized approximate message
passing (BiGAMP) algorithm based on the row sparse channel matrix structure.
This algorithm can jointly detect device activities, estimate channels, and
detect signals in massive multiple-input multiple-output (MIMO) systems by
alternating updates between channel matrices and signal matrices. The signal
observation provides additional information for performance improvement
compared to the existing algorithms. We further analyze state evolution (SE) to
measure the performance of the proposed algorithm and characterize the
convergence condition for SE. Moreover, we perform theoretical analysis on the
error probability of device activity detection, the mean square error of
channel estimation, and the symbol error rate of signal detection. The
numerical results demonstrate the superiority of the proposed algorithm over
the state-of-the-art methods in DADCE-SD, and the numerical results are
relatively close to the theoretical analysis results.
