Automatic Driving Network(ADN)Solution

Autonomous Driving Network

Autonomous Driving Network (ADN) resolves the TCO structure problems of telecom networks with system-level innovations. ADN utilizes network automation, AI, and digital twin technologies to simplify networks and operation, helping operators improve network quality, as well as increase efficiency and Agile Business.

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Network Challenges
  • 01

    Comprehensive business

    Enter multi-play network business by evolving from B2C to B2X covering myriad of vertical markets like drones, IoV, and AR/VR and more.

  • 02

    Complex O&M

    Coexistence of 2G, 3G, 4G and 5G mobile networks has created multifold of complexity in all aspects of network O&M.

  • 03

    Extensive connections

    Intelligent connectivity has enabled extension to connection density by 100 times, connection volume by 1000 times and mobility speed of up to 150 km/h.

  • 21

    Enhance O&M

    Scenario-specific and open programmable APIs improve O&M integration efficiency, reduce job nodes that need manual intervention, shorten service time, and minimize errors caused by manual operations.

  • 22

    Optimize resource utilization

    A network traffic forecast model is generated through AI training to implement real-time network resource scheduling and topology management based on the network traffic trend, optimizing network resource utilization.

  • 23

    Increase energy efficiency

    AI training is utilized to forecast network load and generate energy consumption models for base stations and data centers, enabling dynamic energy provisioning based on network loads.

  • 24

    Improve user experience

    The cloud and network collaborate under the user-unaware configuration to implement real-time service provisioning, on-demand selection, SLA assurance, and zero interruption, achieving optimal user experience with the right service at the right location.


Use Cases