One-bit autocorrelation estimation with non-zero thresholds
Journal
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Journal Volume
2021-June
Pages
4520-4524
Date Issued
2021
Author(s)
Abstract
One-bit quantization has received attention due to its simplicity, low cost, and ability to recover the autocorrelation of the unquantized data. In the past, the autocorrelation estimation from one-bit data can be done in two separate stages. One is the power estimation by using one-bit quantizers of non-zero thresholds, while the other focuses on estimating the normalized autocorrelation with one-bit quantizers of a zero threshold. However, the overall hardware cost increases in this approach. This paper presents an autocorrelation estimator based on a one-bit quantizer with a non-zero threshold. The proposed method depends purely on a one-bit quantizer and its output data. Our method first infers the power information and then estimates the normalized autocorrelation by polynomial root-finding. The autocorrelation estimate is obtained by combining the power and the normalized autocorrelation. Numerical simulations show that the proposed method exhibits similar behavior to an estimator based on the unquantized data, with a 5dB loss in the estimation error. ? 2021 IEEE
Subjects
Autocorrelation estimation
Hermite polynomials
Mehler's formula
One-bit quantization
Price's theorem
Numerical methods
Signal processing
Autocorrelation estimator
Estimation errors
Hardware cost
Output data
Polynomial root findings
Power estimations
Quantizers
Autocorrelation
Type
conference paper