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

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

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.

Details

OriginalspracheEnglisch
TitelProceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers (IEEE)
Seiten31-35
Seitenumfang5
ISBN (elektronisch)978-1-6654-5245-8
PublikationsstatusVeröffentlicht - 2023
Peer-Review-StatusJa

Publikationsreihe

ReiheIEEE/SP Workshop on Statistical Signal Processing (SSP)
Band2023-July

Workshop

Titel22nd IEEE Statistical Signal Processing Workshop
KurztitelSSP 2023
Veranstaltungsnummer22
Dauer2 - 5 Juli 2023
Webseite
OrtThe Hanoi Club Hotel & VinUniversity
StadtHanoi
LandVietnam

Schlagworte

Schlagwörter

  • 1-bit quantization, estimation, faster-than-Nyquist signaling, iterative algorithms, phase noise