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Partially Linear Bayesian Estimation Using Mixed-Resolution Data
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DOI：10.1109/LSP.2021.3125273.png)
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Abstract
En 中文
In this letter, we consider Bayesian parameterestimation using mixed-resolution data consisting of both analog and 1-bit quantized measurements. We investigate the use of the partially linear minimum mean-squared-error (PL-MMSE) estimator for this mixed-resolution scheme. The use of the PL-MMSE estimator, proposed for general models with straightforward and complicated parts, has not been demonstrated for quantized data. We derive closed-form analytic expressions for the linear minimum mean-squared-error (LMMSE) and for the PL-MMSE estimator for the mixed-resolution scheme with linear Gaussian orthonormal measurements. We discuss the properties of the proposed PL-MMSE estimator and show that in this case, the PL-MMSE is the sum of a linear function of the quantized measurements and a general Borel measurable function of the analog measurements. In the simulations, we show that the PL-MMSE estimator outperforms the LMMSE estimator for the problem of channel estimation in multiple-input-multiple-output (MIMO) communication systems with mixed analog-to-digital converters (ADCs).
Keywords:
Q measurement
Estimation
Quantization (signal)
Closed-form solutions
Channel estimation
Wireless sensor networks
Bayes methods
Bayesian estimation
linear minimum mean-squared-error (LMMSE) estimator
partially linear MMSE (PL-LMMSE) estimator
quantization
mixed-resolution data
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W