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تسجيل مستخدم جديدLow-complexity Architecture for AR(1) Inference
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In this Letter, we propose a low-complexity estimator for the correlation coefficient based on the signed $operatorname{AR}(1)$ process. The introduced approximation is suitable for implementation in low-power hardware architectures. Monte Carlo simulations reveal that the proposed estimator performs comparably to the competing methods in literature with maximum error in order of $10^{-2}$. However, the hardware implementation of the introduced method presents considerable advantages in several relevant metrics, offering more than 95% reduction in dynamic power and doubling the maximum operating frequency when compared to the reference method.
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