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

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