Skip to main content
Kent Academic Repository

Low-Power RF Signal Detection Using a High-Gain Tunable OEO Based on Equivalent Phase Modulation

Shao, Yuchen, Han, Xiuyou, Ye, Qing, Zhu, Boqin, Dai, Yitang, Wang, Chao, Zhao, Mingshan (2019) Low-Power RF Signal Detection Using a High-Gain Tunable OEO Based on Equivalent Phase Modulation. Journal of Lightwave Technology, 37 (21). pp. 5370-5379. ISSN 0733-8724. (doi:10.1109/JLT.2019.2939666) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:92174)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL:
https://doi.org/10.1109/JLT.2019.2939666

Abstract

A novel photonic method for low power radio-frequency (RF) signal detection by a tunable optoelectronic oscillator (OEO) with high gain based on equivalent phase modulation (EPM) is proposed and experimentally demonstrated. A dual-parallel Mach-Zehnder modulator is utilized to generate the EPM signal with a controllable optical carrier to sideband ratio, by which the gain to the RF signal by the OEO loop can be enhanced. A phase-shifted fiber Bragg grating (PS-FBG) with a narrow-band notch in its reflection band implements the phase modulation to intensity modulation conversion by removing one sideband of the EPM signal. The gain and noise performance of the detection system are theoretically analyzed and simulated. In the experiment, the maximum gain of 10.4 dB is obtained at 8 GHz, and the detection sensitivity for RF signals from 2 to 18 GHz are within the range of -87.7 to -84.3 dBm by using the developed detection system. Compared to normal phase modulation based scheme, the EPM-based system offers 9.1 dB higher gain. Utility of the proposed approach for detecting modulated RF signal is also investigated and an optimization scheme is discussed.

Item Type: Article
DOI/Identification number: 10.1109/JLT.2019.2939666
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Chao Wang
Date Deposited: 06 Dec 2021 10:47 UTC
Last Modified: 06 Dec 2021 10:47 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/92174 (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.