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These companies currently have access to the the AI-based 5G lifecycle resource management Catalyst. Would you like the following company to keep you updated about their products and services by electronic means?
China Telecom Fufu Information Technology Co., Ltd.
China Telecommunications Corporation
Data Service Technology Co.,Ltd
TM Forum Catalyst project team
Whale Cloud Technology Co., Ltd

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C21.0.210 Status: Completed Info Completed: This project has completed this phase and is no longer active.
11 Review(s)
Average based on 11 review(s)
AI , Data & Insights
5G , AI/Machine Learning , Analytics , Network Automation

The combination of 5G networks and cloud technologies has transformed the telecom industry like never before, with the number of network and cloud resources is proliferating by over 10 times. It has become a headache for a CSP to manage the full lifecycle of a pyramid of physical, virtual and cloud resources from network planning & designing, construction to resource readiness and operation & maintenance. 

when it comes to the resource management, two things will come in mind- velocity and data quality. Traditionally, network resources were mainly kept and recorded manually and the whole process of resource management was tedious, time-consuming and inefficient with a lack of automatic tools; what’s more, all the physical, virtual and cloud resources cannot be updated in-time to enable data integrity and ensure each piece of resource is traceable.

This catalyst aims to demonstrate how AI technologies will fuels automation to enable CSPs, be it a greenfield operator or mature operator, fast-track 5G networks with global network resources managed from end to end, making the network construction collaborative and traceable. 

This Catalyst will demonstrate the following use cases:

  • Leverage AI and big data to address the arduous site selection of base stations during network planning;
  • Machine learning is introduced to automate the Optical Distributed Network (ODN) design and synchronization of all types of physical and virtual network resources; 
  • Deep learning algorithms is used for online learning to generate templates for different scenarios of equipment resource recording;
  • AI image recognition-based construction quality inspection to improve standards compliance and realize automated data collection;
  • AI-enabled intelligent 5G network operation and maintenance where AI is used to assist with fault detection, predictive maintenance, ensure energy-efficient networks and make networks more robust without negatively impacting user perception.