  Sparsely spread code division multiple access (SCDMA) is a promising
non-orthogonal multiple access technique for future wireless communications. In
this paper, we propose a novel trainable multiuser detector called sparse
trainable projected gradient (STPG) detector, which is based on the notion of
deep unfolding. In the STPG detector, trainable parameters are embedded to a
projected gradient descent algorithm, which can be trained by standard deep
learning techniques such as back propagation and stochastic gradient descent.
Advantages of the detector are its low computational cost and small number of
trainable parameters, which enables us to treat massive SCDMA systems. In
particular, its computational cost is smaller than a conventional belief
propagation (BP) detector while the STPG detector exhibits nearly same
detection performance with a BP detector. We also propose a scalable joint
learning of signature sequences and the STPG detector for signature design.
Numerical results show that the joint learning improves multiuser detection
performance particular in the low SNR regime.
