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SK Telecom to Leverage In-House AI-based TANGO OSS for Mobile Networks

23 October 2017
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SK Telecom to Leverage In-House AI-based TANGO OSS for Mobile Networks Image Credit: SK Telecom

SK Telecom announced that it has expanded the application of its own T Advanced Next Generation Operational Supporting System (TANGO) to the mobile networks after it adopted the system to its landline network in last December.

In addition, SK Telecom signed a strategic partnership with Bharti Airtel, the world’s third largest carrier, to deploy TANGO in AI-assisted network operation for Airtel.

TANGO is an AI-assisted network operation system with big data analytics and machine learning capabilities, a result of SK Telecom’s two year-long effort of development. It delivers the automated detection of issues on the network, the troubleshooting of the problems and the optimization of the performance.

The system uses machine learning technology to optimize the quality of the network operation. Based on the breakdown of network traffic information by area and period, it offers the automated optimization of the network operations and notify with the best solution for any anomalies on the network, improving the efficiency of the network management.

Also, the system enhances the accuracy in the network management by measuring the quality of the network operations that customers experience in the use of the network, based on the real-time analysis of the performance. Moreover, it features virtualization capabilities, helping mobile carriers to adopt new network capabilities, which include IoT and 5G.

Park Jin-hyo, SVP and Head of Network Technology R&D Center, Sk Telecom
The AI-assisted network operation technology based on big data analytics will be essential in the 5G era. SK Telecom will continue to improve the functionality of TANGO aiming at providing the best-performing network for customers to enjoy.

Ray is a news editor at The Fast Mode, bringing with him more than 10 years of experience in the wireless industry.

For tips and feedback, email Ray at ray.sharma(at), or reach him on LinkedIn @raysharma10, Facebook @1RaySharma


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