Joint Sampling Frequency Offset Estimation and Compensation Algorithms Based on the Farrow Structure

TL;DR

This paper introduces a low-complexity, fully time-domain joint SFO estimation and compensation algorithm based on the Farrow structure, applicable to arbitrary bandlimited signals without Fourier transforms.

Contribution

It proposes a novel joint estimation framework using the Farrow structure that simplifies implementation and enhances robustness against synchronization impairments.

Findings

Accurate SFO estimation over wide conditions

Linear complexity scaling with sample size

Effective compensation demonstrated on real and simulated signals

Abstract

This paper presents joint sampling frequency offset (SFO) estimation and compensation algorithms based on the Farrow structure. Unlike conventional approaches that treat estimation and compensation separately, the proposed framework exploits the interpolator structure to enable a low-complexity, fully time-domain solution applicable to arbitrary bandlimited signals, without imposing constraints on the waveform or requiring Fourier transform based processing. The estimation stage can operate on a real-valued component of a complex signal and supports the simultaneous estimation of SFO and sampling time offset, while being inherently robust to other synchronization impairments such as carrier frequency offset. The proposed estimation algorithms rely on two complementary methods, specifically, Newton's method and iterative least-squares formulation. The implementations of the estimators are presented and the overall computational complexity is analyzed, showing that the complexity scales only linearly with the number of samples employed. Numerical results for real and complex multisine and bandpass-filtered white noise signals demonstrate accurate estimation and effective compensation over a wide range of operating conditions, confirming the flexibility and efficiency of the proposed approach. Moreover, the influence of the Farrow structure approximation error on the SFO estimation accuracy is investigated.