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The Future of Broadband Access Network Architecture and Intelligent Operations

This paper presents an overview of the evolution towards a broadband optical access network. Network operations transformation with emphasis on automated broadband service monitoring, fault detection, fault recovery, and fault prediction is also discussed. However, in order to support broadband access network evolution and to accelerate telco operations transformation, traditional network operations, administration and maintenance (OAM) methods will not be sufficient. Therefore, we describe our key research contributions regarding new approaches to support closed-loop broadband network OAM that will eliminate human touches and automate non-physical fault recovery for daily routine tasks. We have designed and implemented a network knowledge engine (NKE) centered software framework to enable data collection, correlation, and analysis for troubleshooting complex issues in the broadband access network. Our solutions will address the following network management domains, including (a) knowledge management, (b) surveillance management, (c) incident management, and (d) problem management. In this paper, we focus on auto proactive and predictive trouble management which can verify and confirm a passive device problem location by NKE. We will describe a step-by-step methodology to control data process flow, optical fiber path discovery management, and auto-detection of failed passive devices by using the following techniques: (a) big data analytics, (b) knowledge graph, (c) machine learning (ML), and (d) artificial intelligence (AI) prediction rule and learning policy involving the spatial-temporal algorithm. With combinations of these technique, our preliminary study indicates that the accuracy of auto fault detection is 93%, while the false positive rate is ~0.01%. As results, we demonstrated the promise of this novel technology framework in supporting transformation from expert’s domain knowledge into machine knowledge.