Autonomous 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.


  • Network AI unit iMaster NAIE

    Integrates AI knowledge at the network layer, provides multiple cloud services such as data lake, model development and training, as well as model inference, and empowers ADN with simplified network AI application development.


  • Intelligent cross-domain O&M unit iMaster AUTIN

    Runs on the digital O&M platform OWS, and utilizes AI, big data, and cloud technologies to help carriers implement all-online, automated, and intelligent O&M, as well as enhance O&M personnel skills.


  • Network management and control unit (MBB) iMaster MAE

    Focuses on mobile network scenarios such as base station/feature deployment, network performance monitoring and improvement, fault analysis and handling, service provisioning, as well as network energy saving, and introduces AI capabilities from the cloud, network, and site to enable full-scenario mobile ADN.


  • Network management and control unit (FBB) iMaster NCE

    Focuses on fixed network scenarios such as 5G transport, premium broadband, premium private line, DCN, and enterprise campus; integrates manager and controller to translate business intents into network operations, and provides open APIs to achieve quick IT integration, making networks simple, smart, open, and secure.


Use Cases