A Variational Bayesian Perspective on MIMO Detection with Low-Resolution ADCs
A Variational Bayesian Perspective on MIMO Detection with Low-Resolution ADCs
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DOI：
10.1109/ieeeconf56349.2022.10052059
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Ly V. Nguyen;A. L. Swindlehurst;D. Nguyen
中科院分区：
文献类型：
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作者：
Ly V. Nguyen;A. L. Swindlehurst;D. Nguyen
提出了一种基于变分贝叶斯(VB)推理框架的大规模多输入多输出(MIMO)系统的数据检测方法。在信道状态信息(CSI)已知的情况下，推导了匹配滤波量化VB(MF-QVB)和线性最小均方误差量化VB(LMMSE-QVB)检测方法。与传统的基于VB的检测方法假设加性噪声的二阶统计量是已知的不同，我们提出将噪声方差/协方差矩阵作为未知随机变量浮动，用于解释噪声和残留的用户间干扰。最后，通过数值实验表明，本文提出的基于VB的方法不仅具有较好的性能，而且明显优于已有的方法。
This paper proposes data detection methods for massive multiple-input multiple-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs) based on the variational Bayes (VB) inference framework. We 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. 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. Finally, we show via numerical results that the proposed VB-based methods provide robust performance and also significantly outperform existing methods.