Five Core Applications of AI in FTTx All-Optical Networks

The application of AI in FTTx all-optical networks is extensive, comprehensively propelling the network towards L4 autonomous intelligence across the domains of planning, construction, optimization, maintenance, and operations.

1. Intelligent Network Planning: Precise Expansion, Improving Resource Utilization

In the network planning phase, AI algorithms such as deep learning and machine learning can be employed to optimize the construction planning and resource allocation of fiber networks, thereby reducing resource waste. In specific practice, the FTTx network collects second-level traffic data from Optical Network Units (ONUs) and PON ports over a 14-day period via Telemetry technology. It then utilizes a traffic congestion prediction algorithm model to calculate the traffic over-limit index for PON ports, generating a recommended PON port expansion analysis report to guide resource optimization and precise capacity expansion. This approach can increase the automatic resource optimization rate to 86%, significantly enhancing network operational efficiency.

2. Intelligent Network Optimization: Resolving Wi-Fi Co-Channel Interference, Boosting Throughput

In Wi-Fi scenarios, when multiple Access Points (APs) operate simultaneously, they are prone to being on the same channel. The resulting co-channel interference degrades network performance, impacts user experience, and can even lead to service interruptions. ZTE innovatively applies genetic algorithms to network tuning. Genetic algorithms are heuristic search algorithms inspired by Darwin's theory of natural selection—offspring inherit some characteristics from their parents, and offspring with higher fitness have a greater probability of survival. Through iterative selection, an optimal solution can be found. In multi-AP scenarios, based on the historical load and interference data of each AP, AI genetic algorithms are applied to perform global intelligent optimization of network-wide channels, power, and bandwidth. This enables one-click Wi-Fi optimization, reduces the frequency of on-site fault handling visits, and simultaneously increases overall network throughput by 15%.

3. Intelligent Energy Efficiency Optimization: Dynamic Energy Saving, Supporting "Dual Carbon" Goals

The AI-enhanced PON network intelligent dynamic energy-saving solution adopts an architecture of "unified analysis, control, and assessment by the network management system + local decision-making and automatic execution by network elements." By comprehensively evaluating the factors affecting the energy consumption of Optical Line Terminal (OLT) equipment, it intelligently controls the dynamic sleep and wake-up of OLT functional modules, achieving dynamic adjustment of equipment operating states. This solution can effectively save energy and reduce emissions, create green, high-quality home broadband, support the national "Dual Carbon" goals, and help operators gain a competitive edge in low-carbon initiatives.

4. Intelligent Network Maintenance: Shifting from "Device Management" to "User Experience"

The core transformation in network maintenance is a strategic upgrade from "technology-centric" to "user-centric." This means the goal of network maintenance is no longer merely ensuring device operation but guaranteeing that users receive a stable, smooth, and expected experience when using network services.

User Experience Restoration & Quality Issue Localization: Through intelligent boards deployed on OLTs, per-user, per-service perception data is extracted in-flow and transmitted to the cloud in real-time. Leveraging the analytical and reasoning capabilities of AI large models, user internet experience is highly accurately reconstructed. This enables precise identification of users experiencing quality degradation, localization of the root cause of network quality issues, and automatic generation of rectification suggestions.

Fault Diagnosis Acceleration: An AI-based home broadband fault diagnosis agent can quickly and accurately locate fault points by analyzing network operational data, greatly improving fault handling efficiency. This drives the evolution of network operations and maintenance from "device-oriented, management-oriented" to "user-oriented, experience-oriented," enabling proactive maintenance.

5. Intelligent Precision Marketing: Building User Profiles, Quantifying Potential

Leveraging a high-dimensional feature library deployed on OLTs, the system can identify over 18,000 service types, extract multi-dimensional user features in-flow, and integrate massive data from the Operations domain (O-domain) and Business domain (B-domain) to build an AI potential customer mining model. This achieves precise and quantifiable potential exploration. This approach comprehensively enhances the deep perception and intelligent analysis capabilities of "people, things, and networks," builds comprehensive, accurate, and personalized user profiles, increases marketing precision, and injects new momentum into the development of operators' market services.