As 5G and AI continue to transform the telecommunications landscape, ZTE has rolled out solutions to build an L4 autonomous network powered by AI. With a focus on four key areas and six innovative projects, this collaboration is set to revolutionize broadband operations by driving cost savings, boosting efficiency, and accelerating revenue growth in the digital age.
Autonomous network planning: AI-powered resource optimization
Traditional PON networks often faced inefficiencies due to poor resource allocation, with some ports underutilized while others were congested — an issue that was difficult to address manually. The ZTE autonomous network solution uses AI-driven traffic analysis and user growth forecasting to automatically identify underused or overloaded PON ports. By visualizing optimization strategies, the solution eliminates the need for manual intervention, activating dormant resources and increasing PON port utilization by 23%. This innovation demonstrates how AI shifts network planning from a reactive to a proactive approach.
Autonomous network operation: AI-driven fault diagnosis and self-healing
– Fast Fault Diagnosis and Self-Healing at the Service Level
As home broadband usage increases, delays in fault resolution due to manual root cause analysis have become a major challenge. The ZTE solution leverages intelligent OLT cards to collect end-to-end data (from the home to the content source), enabling precise identification of root causes for quality degradation across seven network segments. The solution automatically resolves soft failures, like Wi-Fi configuration issues, reducing fault recovery time by 30% and minimizing on-site maintenance visits.
– LLM-Enabled Natural Language Fault Diagnosis
Traditionally, content source and user analysis with low QoE relied heavily on senior engineers, which limited efficiency. By integrating LLM (Large Language Model) and Text-to-SQL, the ZTE autonomous network solution enables natural language interactions for data mining and root cause analysis. It generates diagnostic reports and intuitive interfaces, reducing technical barriers and significantly boosting operational efficiency.
Autonomous network optimization: Proactive quality enhancement & energy savings
– From KPI to Customer Experience Management (CEM)
Traditional network KPIs don’t always reflect the true user experience, as many dissatisfied users remain silent. The ZTE CEM platform, combined with OLT’s built-in intelligent cards, leverages big data and AI to provide detailed user experience profiles. By breaking down data silos and utilizing knowledge graphs, the solution proactively identifies users experiencing quality degradation and uncovers the root causes, resulting in a 20% reduction in customer complaints.
– Intelligent Energy Management
As OLT scale expands, manual energy-saving efforts become increasingly prone to errors. The autonomous network solution analyzes PON traffic and equipment energy consumption to generate automated energy-saving plans, achieving 95% automation and reducing average equipment power consumption by 10%. This solution strikes the perfect balance between performance and eco-friendly operations.
Autonomous network marketing: AI-driven precision marketing
Traditional marketing efforts often had a success rate of just 3%, failing to capture personalized customer needs. The ZTE solution leverages customer experience data to create AI-driven “diamond model” user profiles, which automatically recommend tailored packages. Through testing and validation, the marketing success rate can be improved to 10%-30% using AI-powered user profiles, demonstrating how AI transforms marketing from a one-size-fits-all approach to a precision-driven strategy, enhancing both user satisfaction and revenue.
Moving forward, ZTE will intensify its development of AI agents, LLMs, and other cutting-edge technologies, driving innovation in autonomous networks. ZTE aims to lead the digital transformation of telecoms and set new industry standards for intelligent, efficient, and sustainable network operations.
