Baseband processing algorithms often require knowledge of the noise power, signal power, or signal-to-noise ratio (SNR). In practice, these parameters are typically unknown and must be estimated. Furthermore, the mean-square error (MSE) is a desirable metric to be minimized in a variety of estimation and signal recovery algorithms. However, the MSE cannot directly be used as it depends on the true signal that is generally unknown to the estimator. In this paper, we propose novel blind estimators for the average noise power, average receive signal power, SNR, and MSE. The proposed estimators can be computed at low complexity and solely rely on the large-dimensional and sparse nature of the processed data. Our estimators can be used (i) to quickly track some of the key system parameters while avoiding additional pilot overhead, (ii) to design low-complexity nonparametric algorithms that require such quantities, and (iii) to accelerate more sophisticated estimation or recovery algorithms. We conduct a theoretical analysis of the proposed estimators for a Bernoulli complex Gaussian (BCG) prior, and we demonstrate their efficacy via synthetic experiments. We also provide three application examples that deviate from the BCG prior in millimeter-wave multi-antenna and cell-free wireless systems for which we develop nonparametric denoising algorithms that improve channel-estimation accuracy with a performance comparable to denoisers that assume perfect knowledge of the system parameters.
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MSE Analysis of a Multi-Loop LMS Pseudo-Random Noise Canceler for Mixed-Signal Circuit Calibration
This paper applies new analytical techniques to evaluate the stability and mean-square error (MSE) convergence of a multi-loop LMS pseudo-random noise canceller which applies to a variety of known mixed-signal circuit calibration techniques. To the authors' knowledge, it is the first published MSE analysis of any multi-loop LMS system, and, unlike most published MSE analyses of single-loop LMS systems, it does not make any simplifying assumptions. The analysis proves that the noise canceler can be made unconditionally stable by design, and provides guidance on how to choose design parameters to achieve a desired level of noise cancellation.
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- Award ID(s):
- 1909678
- PAR ID:
- 10176883
- Date Published:
- Journal Name:
- IEEE Transactions on Circuits and Systems I: Regular Papers
- ISSN:
- 1549-8328
- Page Range / eLocation ID:
- 1 to 15
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TCSI.2020.2985277
