Comparing Iterative and Least-Squares Based Phase Noise Tracking in Receivers with 1-bit Quantization and Oversampling

TL;DR

This paper compares iterative and least-squares phase noise tracking methods in 1-bit quantized, oversampled receivers, highlighting iterative methods' lower error variance but limited spectral efficiency gains.

Contribution

It introduces a comparison between iterative algorithms and LS estimation for phase noise tracking in 1-bit ADC systems with oversampling.

Findings

Iterative phase noise tracking reduces estimation error variance at high SNR.

Limited spectral efficiency gains for high-order zero-crossing modulation.

Iterative methods outperform LS in phase estimation accuracy.

Abstract

High data rates require vast bandwidths, that can be found in the sub-THz band, and high sampling frequencies, which are predicted to lead to a problematically high analog-to-digital converter (ADC) power consumption. It was proposed to use 1-bit ADCs to mitigate this problem. Moreover, oscillator phase noise is predicted to be especially high at sub-THz carrier frequencies. For synchronization the phase must be tracked based on 1-bit quantized observations. We study iterative data-aided phase estimation, i.e., the expectation-maximization and the Fisher-scoring algorithm, compared to least-squares (LS) phase estimation. For phase interpolation at the data symbols, we consider the Kalman filter and the Rauch-Tung-Striebel algorithm. Compared to LS estimation, iterative phase noise tracking leads to a significantly lower estimation error variance at high signal-to-noise ratios. However, its benefit for the spectral efficiency using zero-crossing modulation (ZXM) is limited to marginal gains for high faster-than-Nyquist signaling factors, i.e., higher order ZXM modulation.