Variational Bayes for Joint Channel Estimation and Data Detection in Few-Bit Massive MIMO Systems
Ly V. Nguyen, A. Lee Swindlehurst, and Duy H. N. Nguyen

TL;DR
This paper introduces variational Bayes based joint channel estimation and data detection techniques for low-resolution ADC massive MIMO systems, addressing non-linearity and interference challenges.
Contribution
It develops novel VB inference methods for joint estimation and detection in low-resolution ADC MIMO systems, including noise variance modeling for improved robustness.
Findings
Proposed VB methods outperform existing detection techniques.
The methods are robust to noise and interference.
Numerical results validate the effectiveness of the approaches.
Abstract
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…
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Taxonomy
TopicsAdvanced Wireless Communication Techniques · Advanced MIMO Systems Optimization · Direction-of-Arrival Estimation Techniques
