  This work studies multiuser detection for one-bit massive multiple-input
multiple-output (MIMO) systems in order to diminish the power consumption at
the base station (BS). A low-complexity near-maximum-likelihood (nML) multiuser
detection algorithm is designed, assuming that each BS antenna port is
connected with a pair of single-bit resolution analog-to-digital converters
(ADCs) and each user equipment (UE) transmits symbols from a quadrature
amplitude modulation (QAM) constellation. First, a novel convex program is
formulated as a convex surrogate of the ML detector and subsequently solved
through an accelerated first-order method. Then, the solution of the convex
optimization problem is harnessed to solve a refined combinatorial problem with
reduced search space, requiring non-exponential complexity on the number of the
UEs. Judicious simulation study corroborates the efficacy of the resulting
two-phase detection algorithm. The proposed two-phase algorithm can achieve
symbol error rate (SER) performance close to the ML detector, with
significantly reduced computation cost compared to the nML detection schemes in
prior art.
