One-Bit MIMO Detection: From Global Maximum-Likelihood Detector to Amplitude Retrieval Approach

TL;DR

This paper introduces a global branch-and-bound algorithm for one-bit MIMO detection and a new amplitude retrieval approach, addressing the challenges of amplitude loss due to one-bit quantization in future 6G communication systems.

Contribution

It proposes the first guaranteed global solution algorithm for one-bit ML MIMO detection and a novel amplitude retrieval method that simplifies detection.

Findings

The branch-and-bound algorithm guarantees global optimality.

The amplitude retrieval approach simplifies detection with efficient algorithms.

Simulations confirm the effectiveness and efficiency of the proposed methods.

Abstract

As communication systems advance towards the future 6G era, the incorporation of large-scale antenna arrays in base stations (BSs) presents challenges such as increased hardware costs and energy consumption. To address these issues, the use of one-bit analog-to-digital converters (ADCs)/digital-to-analog converters (DACs) has gained significant attentions. This paper focuses on one-bit multiple-input multiple-output (MIMO) detection in an uplink multiuser transmission scenario where the BS employs one-bit ADCs. One-bit quantization retains only the sign information and loses the amplitude information, which poses a unique challenge in the corresponding detection problem. The maximum-likelihood (ML) formulation of one-bit MIMO detection has a challenging likelihood function that hinders the application of many high-performance detectors developed for classic MIMO detection (under high-resolution ADCs). While many approximate methods for the ML detection problem have been studied, it lacks an efficient global algorithm. This paper fills this gap by proposing an efficient branch-and-bound algorithm, which is guaranteed to find the global solution of the one-bit ML MIMO detection problem. Additionally, a new amplitude retrieval (AR) detection approach is developed, incorporating explicit amplitude variables into the problem formulation. The AR approach yields simpler objective functions that enable the development of efficient algorithms offering both global and approximate solutions. The paper also contributes to the computational complexity analysis of both ML and AR detection problems. Extensive simulations are conducted to demonstrate the effectiveness and efficiency of the proposed formulations and algorithms.