  There has been growing interest in implementing massive MIMO systems by
one-bit analog-to-digital converters (ADCs), which have the benefit of reducing
the power consumption and hardware complexity. One-bit MIMO detection arises in
such a scenario. It aims to detect the multiuser signals from the one-bit
quantized received signals in an uplink channel. In this paper, we consider
one-bit maximum-likelihood (ML) MIMO detection in massive MIMO systems, which
amounts to solving a large-scale nonlinear integer programming problem. We
propose an efficient global algorithm for solving the one-bit ML MIMO detection
problem. We first reformulate the problem as a mixed integer linear programming
(MILP) problem that has a massive number of linear constraints. The massive
number of linear constraints raises computational challenges. To solve the MILP
problem efficiently, we custom build a light-weight branch-and-bound tree
search algorithm, where the linear constraints are incrementally added during
the tree search procedure and only small-size linear programming subproblems
need to be solved at each iteration. We provide simulation results to
demonstrate the efficiency of the proposed method.
