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，采用单比特模数转换器（ADC）实现大规模MIMO系统的研究日益兴起，因其具有降低功耗和硬件复杂度的优势。在此类场景中，需解决单比特MIMO检测问题——即在上行链路中，从经过单比特量化的接收信号中恢复多用户信号。本文针对大规模MIMO系统中的单比特最大似然（ML）MIMO检测问题展开研究，该问题本质上可归结为求解大规模非线性整数规划问题。我们提出了一种高效的全局算法来解决单比特ML MIMO检测问题。首先将原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，大量线性约束带来了计算挑战。为高效求解该MILP问题，我们定制了轻量级分支定界树搜索算法：在树搜索过程中增量式添加线性约束，每次迭代仅需求解小规模线性规划子问题。仿真结果验证了所提方法的高效性。