Abstract
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).
Original language | English |
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Pages (from-to) | 2202-2206 |
Number of pages | 5 |
Journal | IEEE Signal Processing Letters |
Volume | 28 |
DOIs | |
State | Published - 1 Jan 2021 |
Keywords
- Bayesian estimation
- linear minimum mean-squared-error (LMMSE) estimator
- mixed-resolution data
- partially linear MMSE (PL-LMMSE) estimator
- quantization
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics