Double-Sequence Frequency Synchronization for Wideband Millimeter-Wave Systems with Few-Bit ADCs

TL;DR

This paper introduces two novel low-resolution frequency synchronization methods for mmWave systems with few-bit ADCs, utilizing custom sequence pairs and ratio metrics to robustly estimate CFO despite quantization distortion.

Contribution

The paper proposes new double-sequence synchronization techniques with optimized parameters for low-resolution ADCs in mmWave systems, enhancing CFO estimation accuracy and multi-user applicability.

Findings

Robust CFO estimation with low-resolution ADCs demonstrated through numerical examples.

Analytical characterization of CFO estimation performance under quantization effects.

Optimized sequence design improves multi-user CFO estimation range.

Abstract

In this paper, we propose and evaluate two novel double-sequence low-resolution frequency synchronization methods in millimeter-wave (mmWave) systems. In our system model, the base station uses analog beams to send the synchronization signal with infinite-resolution digital-to-analog converters. The user equipment employs a fully digital front end to detect the synchronization signal with low-resolution analog-to-digital converters (ADCs). The key ingredient of the proposed methods is the custom designed synchronization sequence pairs, from which there exists an invertible function (a ratio metric) of the carrier frequency offset (CFO) to be estimated. We use numerical examples to show that the ratio metric is robust to the quantization distortion. Further, we analytically characterize the CFO estimation performances of our proposed designs assuming a single user. To implement our proposed methods in a multi-user scenario, we propose to optimize the double-sequence design parameters such that: (i) for each individual user, the impact of the quantization distortion on the CFO estimation accuracy is minimized, and (ii) the resulting frequency range of estimation can capture as many users' CFOs as possible. Numerical results reveal that our proposed algorithms provide a flexible means to estimate CFO in a variety of low-resolution settings.