Artificial Intelligence (AI) technology has been increasingly integrated into Radio Access Networks (RAN), thereby boosting the intelligence capabilities of the air interface in mobile communications. Specifically, AI-driven beam management technology achieves multiple performance breakthroughs. These include spectral efficiency improvement and coverage quality enhancement, enabled by dynamic optimization of beam direction and intelligent resource multiplexing. This paper proposes a differentiated service datadriven intelligent adjustment method for 5G-A broadcast beams. By considering service-level performance requirements and traffic popularity, the method dynamically and intelligently adjusts broadcast beam parameters. The method aims to provide differential network coverage guarantee for different services under fixed resources and enhance the precision of broadcast beam adjustment.

This paper investigates the workflow and requirement of telecommunications operators in handling 4G/5G user network quality complaints and proposes a solution that combines large and small models to achieve more intelligent complaint handling. The large model is responsible for comprehensively analyzing unstructured data such as user complaint texts, extracting key information, and understanding user intentions. Small models are used for indepth processing of structured data related to network performance indicators, conducting root cause analysis, and providing targeted solutions. The models and systems are applied to current network operations, significantly reducing network maintenance optimization work orders, saving labor costs, and improving work efficiency.

This paper analyzes the typical service requirements of low-altitude scenarios and proposes an intelligent weight optimization scheme for 5G beams in low-altitude scenarios using artificial bee colonies and genetic algorithms. Based on key indicators such as coverage quality, interference level, and service perception, joint optimization was conducted and validated in low-altitude networking pilot areas, resulting in significant improvements in computational efficiency and optimization results. This scheme achieved the optimal solution for the weight of contiguous areas, resulting in sound application effects.

With the 5G commercial deployments rapidly proceeding, operating mobile networks efficiently has become a great challenge. Self-organizing networks have been proposed to focus on automatically monitoring, analyzing and optimizing networks. Regarding the growing scale and complexity, 5G self-organizing networks confront the challenge to handle the massive data. Therefore, AI models are urgently needed to enable end-to-end network automation. In this demonstration, benefiting from the open data interfaces of the standardized NWDAF within the 5GC network, we implement a containerized network data analytic framework embedding Docker-based AI model containers into 5G networks. Furthermore, we simulate a use case automatically monitoring and optimizing user-level QoE in real time and simulation results are presented.

With the increasing market competition, telecom operators need to improve the level of services, ensure the quality of experience, as well as reduce the cost of enterprise. Therefore it is crucial to evaluate the value of telecom customers accurately. The traditional method of telecom customer value assessment is mainly based on ARPU (Average Revenue Per User), which is one-dimensional and cannot evaluate customer value comprehensively. This paper proposes a multidimensional customer value assessment method, including two perspectives, Current value and Potential value to improve the accuracy and comprehensiveness of evaluation. Also, an intelligent method based on swarm intelligence algorithm is presented to calculate the indicator weight for each characteristic field of customer value. The practical results show that the algorithm, which is applied to realistic scenarios of network operation, enables telecom operator to achieve a comprehensive and accurate customer value assessment in many issues such as customer churn warning and accurate recommendation, ultimately increasing the effectiveness and efficiency of decision-making closed loop for telecom operators. At last but not the least, the algorithm and system can benefit other industries to improve their intelligence level of customer service.

With AI and big data technologies, telecom operators are looking to change the traditional O&M model from reactive problem handling to proactive prevention and prediction. This paper proposes a model framework trained on multiple data sources for the 5G wireless network to support proactive O&M tasks based on complaint prediction. By grouping user complaints into base station complaint prediction, the model enhanced precision scores while maintaining high recall scores. The model has been integrated into the operator’s work order system to support intelligent operational optimization workflow.

When the data service latency of mobile network is too large, it will cause problems such as slow page opening, game stuck, video stuck and seriously affect user perception. Therefore, optimizing the network and reducing latency become one of the main tasks in mobile network. This paper researches on the analysis method of 5G data service latency problem. A set of analysis methods, which are for problem demarcation and localization, are provided to support network operation and maintenance personnel in improving user perception, focusing on the key performance of wireless and core network networks that affect the service.

We are going to enter the most intelligent era than ever before. Intelligent electronics network is infiltrating into our life and making it more convenient. Nonetheless, users always want smart home be more intelligent and complete more features. users’ issues are endless. Modular packaging device services and effective choreography algorithms can flexible fit different issues. Many organizations have been proving, implementing and managing business solutions for many specific individual industries. However, when comes to smart home for end users, there are numerous limitations in process, tooling, and skills. In the paper, we provide a lightweight visualized service creating tool and an AI-driven service flow construction model. It helps end users to create services though drag-and-drop, and then deploy new services automatically. And in the end a case study will be introduced.

MDT data have been widely used for 4G/5G wireless network coverage estimation. Whereas the sparsity of the MDT data makes coverage rate bias when it applied into realistic network coverage analysis. To achieve a more precise coverage estimation, this paper proposes an approach that adding geographical and landform information to network coverage estimation in order to refine the coverage rate. An attention U-Net model was applied to landforms recognition from online satellite map with low cost. It can effectively assists telecom operators to filter out areas, which are users inaccessible or do not require signal coverage.

With the continuous expansion of network construction, 4G/5G networks have gradually developed into hybrid multi-frequency heterogeneous networks, while the difficulty of inter-RAT mobility assurance is gradually increasing. Traditional interoperability optimization requires enormous labor costs, and the accuracy is low. This paper proposes an AI-based collaborative optimization scheme under multi-frequency heterogeneous 4G/5G networks based on the XGBoost prediction model and DNN algorithm. It aims to comprehensively improve the performance of different users in multi-frequency heterogeneous 4G/5G networks in terms of 4G/5G neighborhood re-organization and intelligent optimization of 4G/5G interoperability parameters. The results show that the proposed scheme has high accuracy and strong generalization, which is critical in improving user mobility perception under complex network structures. The scheme contributes to the network operators’ efficiency improvement and intelligent transformation process.