Enhanced Uplink Data Detection for Massive MIMO with 1-Bit ADCs: Analysis and Joint Detection

TL;DR

This paper develops an analytical framework for uplink data detection in massive MIMO systems with 1-bit ADCs, introducing a novel joint detection strategy that improves performance over conventional methods.

Contribution

It introduces a new analytical approach for 1-bit quantized massive MIMO detection and proposes a joint detection method that leverages inter-user symbol dependencies.

Findings

MMSE outperforms MRC in symbol error rate.

Proposed LMMD receiver surpasses conventional receivers.

Joint detection strategies significantly improve performance.

Abstract

We present a new analytical framework on the uplink data detection for massive multiple-input multiple-output systems with 1-bit analog-to-digital converters (ADCs). We first characterize the expected values of the soft-estimated symbols (after the linear receiver and prior to the data detection), which are affected by the 1-bit quantization during both the channel estimation and the uplink data transmission. In our analysis, we consider conventional receivers such as maximum ratio combining (MRC), zero forcing, and minimum mean squared error (MMSE), with multiple user equipments (UEs) and correlated Rayleigh fading. Additionally, we design a linear minimum mean dispersion (LMMD) receiver tailored for the data detection with 1-bit ADCs, which exploits the expected values of the soft-estimated symbols previously derived. Then, we propose a joint data detection (JD) strategy that exploits the interdependence among the soft-estimated symbols of the interfering UEs, along with its low-complexity variant. These strategies are compared with the robust maximum likelihood data detection with 1-bit ADCs. Numerical results examining the symbol error rate show that MMSE exhibits a considerable performance gain over MRC, whereas the proposed LMMD receiver significantly outperforms all the conventional receivers. Lastly, the proposed JD and its low-complexity variant provide a significant boost in comparison with the single-UE data detection.