Channel Estimation and Data Detection Analysis of Massive MIMO with 1-Bit ADCs

TL;DR

This paper develops an analytical framework for channel estimation and data detection in massive MIMO systems with 1-bit ADCs, providing explicit performance expressions and revealing a fundamental SNR trade-off.

Contribution

It offers closed-form expressions for MSE and symbol detection metrics in 1-bit massive MIMO, enabling precise performance analysis and system optimization.

Findings

Explicit MSE expressions for channel estimation

Closed-form detection performance metrics

Identification of a key SNR trade-off

Abstract

We present an analytical framework for the channel estimation and the data detection in massive multiple-input multiple-output uplink systems with 1-bit analog-to-digital converters (ADCs) and i.i.d. Rayleigh fading. First, we provide closed-form expressions of the mean squared error (MSE) of the channel estimation considering the state-of-the-art linear minimum MSE estimator and the class of scaled least-squares estimators. For the data detection, we provide closed-form expressions of the expected value and the variance of the estimated symbols when maximum ratio combining is adopted, which can be exploited to efficiently implement minimum distance detection and, potentially, to design the set of transmit symbols. Our analytical findings explicitly depend on key system parameters such as the signal-to-noise ratio (SNR), the number of user equipments, and the pilot length, thus enabling a precise characterization of the performance of the channel estimation and the data detection with 1-bit ADCs. The proposed analysis highlights a fundamental SNR trade-off, according to which operating at the right noise level significantly enhances the system performance.