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<p>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 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 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 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, i.e., a channel state information mismatch-aware detector, for comparator network-aided systems by integrating 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 of a multi-user uplink MIMO system.</p>

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Spatially and temporally correlated channel estimation and detection for comparator network-aided MIMO receivers with 1-bit ADCs

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月19日

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  • Luiz Sampaio,
  • Lukas T. N. Landau

摘要

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 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 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 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, i.e., a channel state information mismatch-aware detector, for comparator network-aided systems by integrating 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 of a multi-user uplink MIMO system.