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.

Temporal action proposal generation plays a vital role in the analysis of untrimmed videos and has garnered growing interest from researchers. Nevertheless, the presence of long-term temporal dependencies and the large variation in action durations within untrimmed videos pose significant challenges for accurately localizing action boundaries. To overcome the aforementioned issues, we design a novel Transformer-based Temporal Feature Pyramid Network (TTFPN) tailored for generating action proposals. Specifically, we introduce a local transformer to capture longterm temporal information while reducing computational complexity through the substitution of conventional selfattention with a localized variant. Subsequently, a temporal feature pyramid is built to produce multi-scale representations, enabling the model to effectively handle action instances of varying durations. Based on this temporal feature pyramid, we employ a convolutional network-based predictor to generate action proposals in an anchor-free manner. We evaluate TTFPN on THUMOS14, a standard benchmark for temporal action detection, to validate its effectiveness. The results show that TTFPN achieves competitive performance and significantly outperforms previous methods.

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.

With the gradual commercialization of 5G VoNR, VoLTE and VoNR will become the main methods of voice services. How to efficiently evaluate the quality of voice service is the focus of telecom operators. This paper proposes an intelligent combined evaluation method of VoLTE and VoNR voice quality based on artificial neural network. In the proposed method, the artificial neural network model is fitted by the call level time slice sample data of voice, and then the prediction model is established. The prediction results of voice quality of mobile networks are obtained by using the prediction model at call level, grid level and area level. Meanwhile, the proposed method can address the shortcomings of traditional evaluation method based on road test, such as high cost, low timeliness and limited area. Finally, through theoretical verification and comparison with the real test results, the effectiveness of the prediction method is verified.

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.

The high popularity of 5G has spawned a large number of emerging application scenarios and diversified business models, meanwhile, it also leads to the increase in network capacity. The research on 5G network capacity has become an important topic to improve the user perception. This paper analyzes the future capacity trend and development characteristic model of 5G, and then determines the four dimensions for evaluating 5G network capacity. Based on each dimension, this paper locates key indicators, and creatively puts forward the concept of experience satisfaction. Furthermore, this paper researches and recommends the capacity expansion thresholds for 3.5G and 2.1G respectively, using the big data fitting method. In addition, this paper also finds the internal relationship between these key indicators, and give the recommended capacity expansion threshold for each type of cell. A reasonable and accurate capacity expansion threshold is can effectively use the limited capacity expansion investment as well as improve user perception of 5G network.