AI and Large-Model Technologies Enable Digital and Intelligent Transformation of Communication Networks

With the continuous progress of science and technology, especially the rapid development of artificial intelligence (AI) and large-model technologies, the telecom industry is witnessing unprecedented changes. From intelligent network eployment to automated O&M management, AI and large-model applications have deeply penetrated all aspects of communication networks. Communication networks are advancing towards digitalization, intelligence, and automation to better serve the digital and intelligent transformation of the economy and society.

Why Communication Networks Need AI

Mobile communication networks have become increasingly complex in recent years, the communication service ecosystem has diversified, and communication network infrastructure and service systems need to address more complicated scenarios. In these contexts, the traditional manual rule pre-definition and execution processing and management capabilities fail to meet the requirements. Achieving more efficient and intelligent solutions through AI technology has become an inevitable trend in communication network development.

On one hand, AI can rapidly identify and resolve issues by analyzing vast amounts of network data, offering round-the-clock monitoring and management for communication networks through automated monitoring, prediction, and maintenance capabilities. This improves network O&M efficiency and greatly reduces the utilization of human resources. On the other hand, AI can extract useful information from massive data through deep learning and data mining technologies to provide decision-making support and enhance network deployment efficiency. The AI technology can automate and intelligently optimize the utilization of network resources, thereby enhancing network performance, improving user experience, and accelerating the innovation and development of communication networks.

Combination of Communication Networks and AI

From the early stages of automated optimization management, reducing manual intervention, to the introduction of machine learning for self-configuration, self-optimization, and self-healing, and further integration of AI and large-model technologies as the intelligent brain, communication networks are evolving towards artificial intelligence. The application of AI and large-model technologies in communication networks is primarily evident in the following aspects.

• Network Planning and Deployment

Through the analysis of extensive network data, user requirements, and service scenarios, AI and large-model technologies automatically generate optimization solutions to help operators and network equipment manufacturers achieve more intelligent and flexible network planning and design. The data analysis and prediction model can be used to evaluate network requirements more accurately, thereby enhancing the accuracy and efficiency of network coverage and capacity configuration.

The AI image and video recognition technology based on deep learning can automatically detect and recognize network devices. For example, on the network construction site, mobile cameras or dedicated apps are used to capture images of equipment installation and the completeness of safety facilities for construction personnel. The AI technology identifies equipment types and locations, providing real-time feedback on audit conclusions. The accuracy of this technology’s identification surpasses manual judgment by a significant margin. This technology not only reduces manual labor but also ensures the correct deployment and connection of devices, thereby guaranteeing high-quality network construction.

• Automatic Network O&M

In automatic network O&M, AI applications can monitor and predict real-time network performance while also offering efficient fault diagnosis and repair solutions. This significantly reduces the need for manual intervention and improves network stability and reliability.

Firstly, AI can identify the operation modes of network devices and links through extensive data analysis and learning. It deeply learns historical data to establish a deep understanding of network behaviors. The process is not only limited to the understanding of network indicators, but also includes the recognition of complex relationships between network devices and the ability to predict potential faults.

Secondly, when a fault occurs in the network, the AI system can respond immediately. For example, if the performance of a network device suddenly degrades or a link is interrupted, the AI system can quickly capture these abnormal signals and locate possible faulty sources based on previous learning and mode identification. This automated fault detection and positioning process can greatly shorten troubleshooting time, ensuring swift resolution of network faults.

Finally, AI can also provide real-time fault diagnosis and repair suggestions. By analyzing the network topology and operational status, the AI system can generate targeted troubleshooting solutions and automatically execute repair operations if necessary. For instance, when the load of a network node is excessively high, the AI system can automatically adjust the load balance to relieve the network pressure and ensure the stable operation of the entire network.

• Intelligent Network Resource Management

AI can be used to analyze real-time network traffic, load, and resource usage, automatically adjusting the allocation and scheduling of network resources. This enables dynamic network optimization and maximizes resource utilization.  

—Intelligent bandwidth management: Some cloud service providers and network operators in China have started using AI to implement intelligent bandwidth management. The AI technology analyzes and manages network traffic, automatically adjusting bandwidth allocation as required to ensure the stability and performance of network services.

—Capacity planning and prediction: Some operators and Internet companies have adopted the AI technology in network capacity planning and prediction. They utilize big data and AI technologies to predict user traffic growth trends, optimize network resource configurations and investment plans, prevent network congestion, and improve user experience.

• Intelligent Network Security Protection

As network security threats grow in number and complexity, AI and large-model technologies play an increasingly important role in network security protection.

In threat identification and prediction, AI technologies use deep learning algorithms to identify and predict threats within networks. They monitor real-time network traffic, analyze detected abnormal behaviors, and raise alarms to prevent network attacks and data leakage.

In trend analysis and prediction, AI technology can use machine learning to analyze global network attack data, predict future threats based on historical attack models, and provide real-time trend analysis and prediction reports. By analyzing historical data, AI can identify the patterns, behaviors, and targets of attackers, offering corresponding early warning and defense suggestions to ensure network operational security.

Looking into Future

In the future, AI and large-model technologies will accelerate the digital and intelligent transformation of communication networks, enabling more intelligent network management and optimization capabilities, precise capacity planning and prediction, robust fault diagnosis and self-healing capabilities, and advanced network security defense. Through continuous technological innovation, ZTE will facilitate the evolution of communication networks towards autonomous and highly intelligent systems.

• Autonomous network: Autonomous network will emerge as an important direction for the future digital and intelligent development of communication networks. At present, communication networks are in the L3 (limited self-intelligence) stage. Utilizing AI and large-model technologies to achieve the ultimate goals of high-level self-intelligence networks and fully autonomous networks is the key for the development of communication networks. However, achieving these goals necessitates ongoing exploration.
• Application of deep learning and enhanced learning: In the future, communication networks will increasingly utilize more cutting-edge technologies such as deep learning and enhanced learning for in-depth analysis and understanding of network data and service scenarios. Through the training of large models, ZTE possesses certain awareness and cognition capabilities, enabling flexible solutions to a range of problems and facilitating the improvement of network intelligence and decision-making capabilities.
• Cross-domain collaboration and ecosystem development: Operators, equipment manufacturers, cloud service providers, and application developers will strengthen cross-domain collaboration and ecosystem development. They will jointly promote the widespread application and innovative development of AI and large-model technologies in communication network construction and operations through collaborative innovation.

To sum up, AI and large-model technologies are becoming key driving forces in the transformation of global communication network construction and operations towards digitalization and intelligence. With continuous innovation and development in AI and large-model technologies, as well as deepening cross-domain cooperation, technological breakthroughs, and ecosystem development, a truly intelligent and inclusive era is on the horizon.