标题： 变分贝叶斯在低比特大规模MIMO系统中联合信道估计和数据检测中的应用 显示英文标题

标题： Variational Bayes for Joint Channel Estimation and Data Detection in Few-Bit Massive MIMO Systems

摘要： 大规模多输入多输出（MIMO）通信使用低分辨率模数转换器（ADCs）是一种有前途的技术，可以在具有可接受的硬件成本和功耗的情况下提供高频谱和能量效率。 然而，使用低分辨率ADCs需要特殊的信号处理方法进行信道估计和数据检测，因为 resulting 系统是严重的非线性的。 本文提出了基于变分贝叶斯（VB）推断框架的大量MIMO系统中低分辨率ADCs的联合信道估计和数据检测方法。 我们首先在信道状态信息（CSI）可用的情况下推导出匹配滤波量化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，并提出两种方法，我们称之为MF-QVB-JED和LMMSE-QVB-JED。 与传统VB-based检测方法假设加性噪声的二阶统计量已知不同，我们提出将噪声方差/协方差矩阵作为未知随机变量，用于同时考虑噪声和剩余用户间干扰。 我们还介绍了QVB框架的实际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VB-based方法提供了稳健的性能，并且显著优于现有方法。 显示英文摘要

摘要： 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 noise variance/covariance matrix as an unknown random variable that is 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.