Optical Fiber Communications is a method of transmitting information using optical fibers, which are thin strands of glass or plastic that can carry light signals over long distances. In optical fiber communications, data is encoded into light pulses, and these pulses are transmitted through the optical fibers. Optical fiber communication offers high bandwidth, low signal attenuation, and resistance to electromagnetic interference, making it a popular choice for high-speed and long-distance data transmission in telecommunications networks.


Simultaneous Fiber Sensing and Communications

We review recent advances aimed at increasing the reach of distributed fiber optic sensing with simultaneous data transmission. We review two methods based on measurement of accumulated phase on telecom signals, and chirp-pulsed DAS with inline amplification and frequency diversity.

Vibration Detection and Localization using Modified Digital Coherent Telecom Transponders

We demonstrate a vibration detection and localization scheme based on bidirectional transmission of telecom signals with digital coherent detection at the receivers. Optical phase is extracted from the digital signal processing blocks of the coherent receiver, from which the vibration component is extracted by bandpass filtering, and the position along the cable closest to the vibration’s epicenter is recovered by correlation. We demonstrate our scheme first using offline experiment with 200-Gb/s DP-16QAM, and we report field trial results over installed fiber to detect real-world vibration events.

First Field Trial of Distributed Fiber Optical Sensing and High-Speed Communication Over an Operational Telecom Network

To the best of our knowledge, we present the first field trial of distributed fiber optical sensing (DFOS) and high-speed communication, comprising a coexisting system, over an operation telecom network. Using probabilistic-shaped (PS) DP-144QAM, a 36.8 Tb/s with an 8.28-b/s/Hz spectral efficiency (SE) (48-Gbaud channels, 50-GHz channel spacing) was achieved. Employing DFOS technology, road traffic, i.e., vehicle speed and vehicle density, were sensed with 98.5% and 94.5% accuracies, respectively, as compared to video analytics. Additionally, road conditions, i.e., roughness level was sensed with >85% accuracy via a machine learning based classifier.