  This paper considers signal detection in massive multiple-input
multiple-output (MIMO) systems with general additive hardware impairments and
one-bit quantization. First, we present the quantization-unaware and Bussgang
decomposition-based linear receivers by generalizing them for the considered
hardware impairment model. We propose an optimization problem to estimate the
uplink data signals by choosing a suitable cost function that treats the
unquantized received signal at the base station as the variable. We exploit the
additional structure of the one-bit quantization and signal modulation by
including proper constraints. To solve the non-convex quadratically-constrained
quadratic programming (QCQP) problem, we propose an ADMM-based algorithm with
closed-form update equations. Then, we replace the harsh projectors in the
updates with their soft versions to improve the detection performance. We show
that the proposed ADMM-based algorithm outperforms the state-of-the-art linear
receivers significantly in terms of bit error rate (BER) and the performance
gain increases with the number of antennas and users.
