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.
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 intelligence capabilities from the cloud, network, and site to enable full-scenario mobile ADN.
Huawei 5G ITA solution automatically plans and optimizes neighboring cells for 5G sites, increasing coverage optimization efficiency fivefold. It adjusts 5G Pattern beams according to population and traffic changes, improving poor-QoE user experience by 10%.
21 local networks have been merged into an E2E network in the Greater Bay Area, reducing network latency from 10 to 6 ms. Users can comprehensively monitor network running status as well as view the latency, traffic, and topology.
Huawei's smart home broadband O&M solution has achieved remarkable results in optical trail fault locating and proactive weak optical signal rectification. Estimates indicate that O&M efficiency will increase by 50%, the home visit rate will reduce by over 30%, and the churn rate will reduce by 30%.
AI Capacity Turbo maximizes capacity potential based on limited spectrum resources. It flexibly integrates with network and site AI algorithms to optimize wireless performance and increase network capacity by 15%.
The AI-enhanced machine learning algorithm analyzes cell coverage, traffic, and interference, iteratively optimizes hundreds of millions of parameters within two weeks, and generates optimal parameter sets for cells, improving user experience by 15%.