Enter multi-play network business by evolving from B2C to B2X covering myriad of vertical markets like drones, IoV, and AR/VR and more.
Coexistence of 2G, 3G, 4G and 5G mobile networks has created multifold of complexity in all aspects of network O&M.
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.
"Take Complexity, Create Simplicity" is the supreme principle of autonomous driving network (ADN), which is practiced throughout product planning, design, and development at Huawei. Collaborative evolution of network and O&M intelligence is the core of ADN, and in this regard Huawei has produced a three-layered open architecture delivering intelligence for networks and platforms for O&M, enabling telecom operators to accelerate their digital transformation.
The three layers are cloud intelligence, network intelligence, and NE intelligence, which combine to achieve effective ADN.
Cloud intelligence: Telecom knowledge assets are aggregated in the cloud, generating an intelligent platform for data training as well as model generation and optimization. The results are then synchronized to the network and NE layers, ensuring the optimal utilization of up-to-date models.
Network intelligence: At the network management and control layer, big data analysis, intelligent algorithms, and service APIs are adopted to achieve service intent automation, network O&M intelligentization, and network servitization.
NE intelligence: A lightweight intelligent inference framework is embedded at the device layer to provide NE-level short-period awareness analysis and inference capabilities, with inference completed in microseconds.
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.
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.
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.
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.
Focuses on fixed network scenarios such as Mobile Backhaul , 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.
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.
MBB Automation Engine (MAE) offers automatic network operations and maintenance capabilities to enable network automation. The production process of MAE-M is customer-oriented and provides scenario-specific apps based on the lifecycle, covering network deployment, maintenance, optimization, and service provisioning. This approach effectively reduces OPEX, optimizes service experience and operation efficiency, and accelerates all-scenario automation of operator workflows, enabling operators to implement network automation.
iMaster MAE-CN is an autonomous driving network solution specifically designed for the core network field. It streamlines the entire process from planning, construction, and maintenance to optimization. It enables operators to achieve the industry's best TTM and TTR through network management, analysis, and control. With iMaster MAE-CN, operators can build 5G core networks that are agile, reliable, and offer a premium user experience.
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