Key result
The proposed 2nd-order SD-VB algorithm delivered the best symbol error rate performance while maintaining the same computational complexity as conventional methods.
Why the study?
Spatial Sigma-Delta architectures can reduce quantization noise and enhance effective resolution of few-bit ADCs, motivating the development of tailored data detection algorithms for massive MIMO systems.
Comparison
Proposed SD-VB algorithms vs unquantized systems, matched-filtering VB with conventional quantization, and LMMSE methods
Design
Simulation study
Authors
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VB-based detectors for few-bit ∑Δ ADCs boost MIMO resolution at key angles; leaves open practical validation in deployed systems.
A novel Sigma-Delta variational Bayes algorithm improves symbol error rate in massive MIMO systems with few-bit ADCs without increasing computational complexity.
Nguyen et al. (2025) studied this question. Sigma-Delta variational Bayes (SD-VB) algorithm vs. unquantized systems, matched-filtering VB with conventional quantization, and LMMSE methods was evaluated on Symbol error rate (SER). The proposed 2nd-order SD-VB algorithm delivered the best symbol error rate performance while maintaining the same computational complexity as conventional methods.
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