  Detection for one-bit massive MIMO systems presents several challenges
especially for higher order constellations. Recent advances in both model-based
analysis and deep learning frameworks have resulted in several robust one-bit
detector designs. Our work builds on the current state-of-the-art gradient
descent (GD)-based detector. We introduce two novel contributions in our
detector design: (i) We augment each GD iteration with a deep learning-aided
regularization step, and (ii) We introduce a novel constellation-based loss
function for our regularized DNN detector. This one-bit detection strategy is
applied to two different DNN architectures based on algorithm unrolling,
namely, a deep unfolded neural network and a deep recurrent neural network.
Being trained on multiple randomly sampled channel matrices, these networks are
developed as general one-bit detectors. The numerical results show that the
combination of the DNN-augmented regularized GD and constellation-based loss
function improve the quality of our one-bit detector, especially for higher
order M-QAM constellations.
