Massive multiple-input multiple-output (MIMO) communications using low-resolution analog-to-digital converters (ADCs) is a promising technology for providing high spectral and energy efficiency with affordable hardware cost and power consumption. However, the use of low-resolution ADCs requires special signal processing methods for channel estimation and data detection since the resulting system is severely non-linear. This paper proposes joint channel estimation and data detection methods for massive MIMO systems with low-resolution ADCs based on the variational Bayes (VB) inference framework. We first derive matched-filter quantized VB (MF-QVB) and linear minimum mean-squared error quantized VB (LMMSE-QVB) detection methods assuming the channel state information (CSI) is available. Then we extend these methods to the joint channel estimation and data detection (JED) problem and propose two methods we refer to as MF-QVB-JED and LMMSE-QVB-JED. Unlike conventional VB-based detection methods that assume knowledge of the second-order statistics of the additive noise, we propose to float the elements of the noise covariance matrix as unknown random variables that are used to account for both the noise and the residual inter-user interference. We also present practical aspects of the QVB framework to improve its implementation stability. Finally, we show via numerical results that the proposed VB-based methods provide robust performance and also significantly outperform existing methods.

中文翻译：

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少比特大规模 MIMO 系统中联合信道估计和数据检测的变分贝叶斯


使用低分辨率模数转换器 (ADC) 的大规模多输入多输出 (MIMO) 通信是一项很有前景的技术，能够以经济实惠的硬件成本和功耗提供高频谱和能源效率。然而，使用低分辨率 ADC 需要特殊的信号处理方法来进行信道估计和数据检测，因为所得系统是严重非线性的。本文提出了基于变分贝叶斯 (VB) 推理框架的具有低分辨率 ADC 的大规模 MIMO 系统的联合信道估计和数据检测方法。我们首先假设信道状态信息（CSI）可用，推导出匹配滤波器量化VB（MF-QVB）和线性最小均方误差量化VB（LMMSE-QVB）检测方法。然后，我们将这些方法扩展到联合信道估计和数据检测（JED）问题，并提出两种我们称为 MF-QVB-JED 和 LMMSE-QVB-JED 的方法。与传统的基于 VB 的检测方法（假设了解加性噪声的二阶统计量）不同，我们建议将噪声协方差矩阵的元素浮动为未知随机变量，用于解释噪声和残差间的关系。用户干扰。我们还介绍了 QVB 框架的实用方面，以提高其实现稳定性。最后，我们通过数值结果表明，所提出的基于 VB 的方法具有稳健的性能，并且显着优于现有方法。