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• W4312992079 abstract "The recovery of the input signal covariance values from its one-bit sampled counterpart has been deemed a challenging task in the literature. To deal with its difficulties, some assumptions are typically made to find a relation between the input covariance matrix and the autocorrelation values of the one-bit sampled data. This includes the arcsine law and the recently proposed modified arcsine law which unleashes a promising performance in covariance recovery by taking advantage of time-varying sampling thresholds. However, the modified arcsine law also assumes input signals are stationary, which is typically a simplifying assumption for real-world applications. In fact, in many signal processing applications, the input signals are readily known to be non-stationary with a non-Toeplitz covariance matrix. In this paper, we propose an approach to extending the arcsine law to the case where one-bit ADCs apply time-varying thresholds while dealing with input signals that originate from a non-stationary process. In particular, the recovery methods are shown to accurately recover the time-varying variance and autocorrelation values. Furthermore, we extend the formulation of the Bussgang law to the case where non-stationary input signals are considered." @default.
• W4312992079 created "2023-01-05" @default.
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• W4312992079 date "2022-01-01" @default.
• W4312992079 modified "2023-10-15" @default.
• W4312992079 title "Covariance Recovery for One-Bit Sampled Non-Stationary Signals With Time-Varying Sampling Thresholds" @default.
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• W4312992079 doi "https://doi.org/10.1109/tsp.2022.3217379" @default.
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