Co-Route Fiber Recognition and Status Diagnosis Based on Integrated Sensing and Communication in 6G Transport Networks

• Published in: IEEE internet of things journal 2024-09, Vol.11 (18), p.29348-29359
• Main Authors: Wang, Zhiwei, Yang, Hui, Li, Yunbo, Yao, Qiuyan, Yu, Tiankuo, Zhang, Chen, Liu, Wenxin, Lin, Wenbo, Zhang, Jie, Liu, Yucong, Cheriet, Mohamed
• Format: Article
• Language: English
• Subjects: 6G mobile communication; Accuracy; Classification; Co-route fiber recognition; Communication; Computer architecture; distributed optical fiber sensing; event classification; Feature extraction; feature extraction algorithm (FEA); Fiber optics; machine learning (ML) models; Network reliability; Optical fiber cables; Optical fiber networks; Optical fiber sensors; Optical fibers; Real time; Recognition; Survivability; Trenches; Vibration; Vibrations
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2024.3414863

The 6G transport network facilitates the Internet of Everything (IoE), carrying numerous services and emphasizing the paramount importance of its reliability. However, within the transport network, the issue of co-route fibers arises. The co-route fibers, encompassing both co-cable and co-trench fibers, presents a significant latent hazard for service disruptions, posing a substantial threat to the seamless connectivity envisioned for the 6G era of pervasive IoE. The segregation of communication and sensing in the transmission network results in mutual interference between communication and sensing signals, rendering it difficult to promptly address sudden fiber interruptions. This article proposes an integrated sensing and communication (ISAC) architecture within transport networks, aiming at the online discernment of co-cable fibers, characterization of fiber optic trenches, and real-time classification of fiber vibration events. In the domain of co-cable fiber identification, our approach has successfully reduced the nuisance alarm rate to an impressive 5.3%, while simultaneously elevating the recognition accuracy to an outstanding 99.7%. As for co-trench fiber identification, our proposed methodology not only facilitates the discernment of co-trench fibers but also achieves an impressive accuracy of 97.7% in classifying fiber trenches. Moreover, in the realm of fiber state prediction, our solution has achieved a remarkable recognition accuracy of 98% across six distinct vibration events. These results underscore the robust performance of the proposed ISAC architecture, which will effectively safeguard the survivability of 6G IoE.