  In this paper, we propose a turbo receiver for joint activity detection and
data decoding in grant-free massive random access, which iterates between a
detector and a belief propagation (BP)-based channel decoder. Specifically,
responsible for user activity detection, channel estimation and soft data
symbol detection, the detector is developed based on a bilinear inference
problem that exploits the common sparsity pattern in the received pilot and
data signals. The bilinear generalized approximate message passing (BiG-AMP)
algorithm is adopted to solve the problem using probabilities of the
transmitted data symbols estimated by the channel decoder as prior knowledge.
In addition, extrinsic information is also derived from the detector to improve
the channel decoding accuracy in the decoder. Simulation results show
significant improvements achieved by the proposed turbo receiver compared with
conventional designs.
