Spatially and Temporally Correlated Channel Estimation and Detection for Comparator Network-Aided MIMO Receivers with 1-bit ADCs

The low-resolution aware linear minimum mean-squared error (LRA-LMMSE) channel estimator, designed for low-resolution MIMO receivers, achieves a notable reduction in mean-squared error (MSE) by incorporating a comparator network. This network comprises multiple simple comparators that generate binary outputs.In this study, we propose the Kalman filter-based channel estimator with comparator networks (KFB-CN) for temporally and spatially correlated channels in MIMO systems utilizing 1-bit analog-to-digital converters (ADCs) and comparator networks. Following a comprehensive mathematical derivation of the real-valued Kalman filter system and observation models, we demonstrate, via numerical simulations, that the KFB-CN surpasses the performance of the Kalman filter-based estimator (KFB) without comparator networks. Furthermore, we present a dynamic comparator network selection algorithm that adjusts the utilized comparators in real-time to account for variations in channel correlation coefficients. Lastly, we propose a robust detector for comparator network-aided systems that integrates the mean-squared error estimated from the Kalman filter channel estimator. Numerical simulations highlight a tenfold improvement in performance with respect to symbol error rate.