In this work, we investigate the capacity of multi-antenna fading channels with 1-bit quantized output per receive antenna. Specifically, leveraging Bayesian statistical tools, we analyze the asymptotic regime with a large number of receive antennas. In the coherent case, where the channel state information (CSI) is known at the receiver's side, we completely characterize the asymptotic capacity and provide the exact scaling in the extreme regimes of signal-to-noise ratio (SNR) and the number of transmit antennas. In the non-coherent case, where the CSI is unknown but remains constant during T symbol periods, we first obtain the exact asymptotic capacity for T<=3. Then, we propose a scheme involving uniform signaling in the covariance space and derive a non-asymptotic lower bound on the capacity for an arbitrary block size T. Furthermore, we propose a genie-aided upper bound where the channel is revealed to the receiver. We show that the upper and lower bounds coincide when T is large. In the low SNR regime, we derive the asymptotic capacity up to a vanishing term, which, remarkably, matches our capacity lower bound.

翻译：本文研究了每根接收天线采用1比特量化输出的多天线衰落信道容量。具体而言，我们利用贝叶斯统计工具，分析了接收天线数量趋于无穷时的渐近特性。在相干场景下（接收端已知信道状态信息），我们完整刻画了渐近容量，并给出了在极端信噪比与发射天线数量条件下的精确缩放规律。在非相干场景下（信道状态信息未知但在T个符号周期内保持不变），我们首先获得了T≤3时的精确渐近容量。随后，我们提出了一种在协方差空间进行均匀调制的方案，并推导出任意分组长度T下的容量非渐近下界。此外，我们构建了接收端获知信道信息的"精灵辅助"上界，并证明当T较大时上下界趋于一致。在低信噪比区域，我们推导出渐近容量至可忽略项，该结果与容量下界具有一致性。