  In most existing grant-free (GF) studies, the two key tasks, namely active
user detection (AUD) and payload data decoding, are handled separately. In this
paper, a two-step dataaided AUD scheme is proposed, namely the initial AUD step
and the false alarm correction step respectively. To implement the initial AUD
step, an embedded low-density-signature (LDS) based preamble pool is
constructed. In addition, two message passing algorithm (MPA) based initial
estimators are developed. In the false alarm correction step, a redundant
factor graph is constructed based on the initial active user set, on which MPA
is employed for data decoding. The remaining false detected inactive users will
be further recognized by the false alarm corrector with the aid of decoded data
symbols. Simulation results reveal that both the data decoding performance and
the AUD performance are significantly enhanced by more than 1:5 dB at the
target accuracy of 10^3 compared with the traditional compressed sensing (CS)
based counterparts
