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.

翻译：近年来，通过一比特模数转换器（ADC）实现大规模MIMO系统的研究兴趣日益增长，该技术具有降低功耗和硬件复杂度的优势。一比特MIMO检测正是在此背景下应运而生——其目标是在上行链路中从一比特量化接收信号中检测多用户信号。本文研究大规模MIMO系统中的一比特最大似然（ML）MIMO检测问题，该问题本质上是求解大规模非线性整数规划问题。我们提出了一种用于求解一比特ML MIMO检测问题的高效全局算法。首先将原始问题重构为具有海量线性约束的混合整数线性规划（MILP）问题，这些线性约束引发了计算挑战。为高效求解该MILP问题，我们定制设计了轻量级分支定界树搜索算法，该算法在树搜索过程中逐步添加线性约束，且每次迭代仅需求解小型线性规划子问题。仿真结果验证了所提方法的高效性。