An Efficient Global Algorithm for One-Bit Maximum-Likelihood MIMO Detection

TL;DR

This paper introduces a fast, global algorithm for one-bit maximum-likelihood MIMO detection in massive MIMO systems, effectively solving a large-scale nonlinear integer programming problem with reduced computational complexity.

Contribution

It reformulates the one-bit ML MIMO detection as a mixed integer linear programming problem and develops a lightweight branch-and-bound algorithm for efficient solution.

Findings

The proposed algorithm significantly reduces computation time.

It effectively handles large-scale nonlinear integer problems.

Simulation results confirm the method's efficiency.

Abstract

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.