Figure 2 - uploaded by Ben Asante
Content may be subject to copyright.
Source publication
The Mobile Telecommunication Network (MTN) among many other telecommunication industries embrace the use of fiber optic technology to deploy their networks to offer services to their customers based on its numerous advantages. The advantages are not limited to its immunity to electromagnetic wave pick up, high capacity of bandwidth for data and voi...
Contexts in source publication
Context 1
... report further emphasize that, in developing markets, increase in mobile penetration benefit GDP growth per capita and boost country productivity. Figure 2. Below shows a trend analysis in increasing growth rate in GDP per capital against average usage per 3G cellular mobile technology connections for some selected countries. ...
Context 2
... the investigation he established that between 1990 and 1992, optical fiber cable failures was the single largest causes of network outages which affected over 50,000 The root causes are the critical event such as lack of proper notification, which lead to the failure. Figure 2.5 and The general outcome of the entire investigation established weak laws as one of the primary reasons for the continuing cutting or damaging of the optical fiber cable. He said, this is because 40% of the total cable failure recorded happened in locations or areas with accurate cable location, proper temporal markers of subsurface cable route (in order words proper prior notification were made to the excavator). ...
Context 3
... Ghana MTNhas deployed an excess of about one thousand two hundred and four kilometers of fiber optic network infrastructure as said by the Senior Manager for Network Field operations in charge of Southern Ghana, ie, (WR & CR). Figure 2.6 below shows a sketch of MTN's fiber layout coverage deployed in the utility corridor or road reservation as acquired in the form of right-of-way in the jurisdiction under study. The layout provides further details showing the backbone footprint of the optical fiber backbone network infrastructure in WR&CR generally. ...
Context 4
... he indicated that its cost the organization up to about Fifty Thousand Ghana Cedis (GHC50, 000) to relocate a kilometer of fiber cable. Figure 2.6(a and b) show a statistical analysis on fiber relocation effected in WR&CR between 2014 and 2016 (August ending) and their corresponding cost associated with it. ...
Context 5
... report further emphasize that, in developing markets, increase in mobile penetration benefit GDP growth per capita and boost country productivity. Figure 2. Below shows a trend analysis in increasing growth rate in GDP per capital against average usage per 3G cellular mobile technology connections for some selected countries. ...
Context 6
... the investigation he established that between 1990 and 1992, optical fiber cable failures was the single largest causes of network outages which affected over 50,000 The root causes are the critical event such as lack of proper notification, which lead to the failure. Figure 2.5 and The general outcome of the entire investigation established weak laws as one of the primary reasons for the continuing cutting or damaging of the optical fiber cable. He said, this is because 40% of the total cable failure recorded happened in locations or areas with accurate cable location, proper temporal markers of subsurface cable route (in order words proper prior notification were made to the excavator). ...
Context 7
... Ghana MTNhas deployed an excess of about one thousand two hundred and four kilometers of fiber optic network infrastructure as said by the Senior Manager for Network Field operations in charge of Southern Ghana, ie, (WR & CR). Figure 2.6 below shows a sketch of MTN's fiber layout coverage deployed in the utility corridor or road reservation as acquired in the form of right-of-way in the jurisdiction under study. The layout provides further details showing the backbone footprint of the optical fiber backbone network infrastructure in WR&CR generally. ...
Context 8
... he indicated that its cost the organization up to about Fifty Thousand Ghana Cedis (GHC50, 000) to relocate a kilometer of fiber cable. Figure 2.6(a and b) show a statistical analysis on fiber relocation effected in WR&CR between 2014 and 2016 (August ending) and their corresponding cost associated with it. ...
Similar publications
The Internet of things (IoT) provides an integration of various sensors and objects that can communicate directly with one another without human intervention. The "things" in the IoT include physical devices, such as sensor devices, which monitor and gather all types of data on machines and human social life. The arrival of the IoT has led to the c...
Citations
Passive optical networks (PONs) have become a promising broadband access network solution thanks to their wide bandwidth, low-cost deployment and maintenance, and scalability. To ensure a reliable transmission, and to meet service level agreements, PON systems have to be monitored constantly in order to quickly identify and localize network faults and thus reduce maintenance costs, minimize downtime, and enhance quality of service. Typically, a service disruption in a PON system is mainly due to fiber cuts and optical network unit (ONU) transmitter/receiver failures. When the ONUs are located at different distances from the optical line terminal, the faulty ONU or branch can be identified by analyzing the recorded optical time domain reflectometry (OTDR) traces. OTDR is a technique commonly used for monitoring of fiber optic links. However, faulty branch isolation becomes very challenging when the reflections originate from two or more branches with similar length overlap, which makes it very hard to discriminate the faulty branches given the global backscattered signal. Recently, machine learning (ML)-based approaches have shown great potential for managing optical faults in PON systems. Such techniques perform well when trained and tested with data derived from the same PON system. But their performance may severely degrade if the PON system (adopted for the generation of the training data) has changed, e.g., by adding more branches or varying the length difference between two neighboring branches, etc. A re-training of the ML models has to be conducted for each network change, which can be time consuming. In this paper, to overcome the aforementioned issues, we propose a generic ML approach trained independently of the network architecture for identifying the faulty branch in PON systems given OTDR signals for the cases of branches with close lengths. Such an approach can be applied to an arbitrary PON system without requiring to be re-trained for each change of the network. The proposed approach is validated using experimental data derived from the PON system.
