期刊
IEEE ACCESS
卷 7, 期 -, 页码 77932-77943出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2921329
关键词
5G and beyond; fronthaul; fault location; deep belief network
资金
- NSFC [61871056]
- Young Elite Scientists Sponsorship Program by CAST (YESS) [2018QNRC001]
- Fundamental Research Funds for the Central Universities [2018XKJC06]
- Open Fund of SKL of IPOC (BUPT) [IPOC2018A001]
- ZTE Research Fund
In face of staggering traffic growth driven by fifth generation (5G) and beyond, optical fronthaul networks which host such connections require efficient and reliable operational environments. Fault location has become one of the primary factors for post-fault responses. In this paper, we propose a Deep Belief Network (DBN) based fault location (DBN-FL) model to locate single-link fault of optical fronthaul network in 5G and beyond. The DBN-FL model contains two phases including the hybrid pre-training phase and the Levenberg Marquardt (LM) algorithm-based fine-tuning phase. In the hybrid pre-training phase, we combine the supervised and unsupervised learning to reduce the demand for training samples. In the fine-tuning phase, the LM algorithm is adopted to fine-tune the DBN-FL model. The experimental results indicate that the proposed DBN-FL model can realize high-accuracy fault location as a classifier (accuracy over 96%), and outperforms traditional deep learning (DL) approaches both in terms of location accuracy and training efficiency.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据