5G non-public network (NPN) can provide customized and dedicated network services for various vertical industries. The operation of 5G NPN is a crucial aspect for the deployment and application of 5G NPN. This paper studies the development of 5G NPN operation. Furthermore, this paper proposes a three-stage evolution path, framework and the guaranteed requirements for 5G NPN operation. Some examples are also provided to achieve NPN optimization goal by the framework. The paper provides insights and guidance for the vertical industries of 5G NPN operation, as well as suggests potential directions for future work on 5G NPN operation.

In recent years, 5G/5G-A technology has fast developed and found widespread deployment, meeting the diverse requirements of application scenarios across various industries. In this paper, we introduce the principle and advantages of 5G/5G-A private network. Then, we introduce the construction of 5G/5G-A private network. Furthermore, we design an intelligent operation system of 5G/5G-A private network, which includes six key modules with over twenty functionalities. This intelligent operation system can effectively support the operation of 5G/5G-A private network. Lastly, this paper introduces the 5G/5G-A private network applications in a realistic vehicle factory.

The growth analysis of enterprises is an important basis for predicting the future development trend of enterprises. For an enterprise itself, the enterprise growth analysis can help the enterprise to understand its own business situation. It can also assist the enterprise to accurately customize the development strategy. As far as the investment market is concerned, the enterprise growth analysis can help investors comprehensively understand the investment target and reduce the investment risk as well as improve the investment benefit. This paper makes a comparative analysis on the growth of 4937 enterprises with all A-shares in different industries from seven dimensions, including competitiveness, profitability, operation ability, debt paying ability, R & D ability, scale expansion ability, enterprise supply chain ability. This paper reveals that there are significant differences in the growth of enterprises in different industries in the post-epidemic era.

In order to meet the needs of data analysis and visualization to assist mobile network operation decision-making, telecom operators have established several mobile network index analysis tools or platforms. However, the network data analysis efficiency of planning, construction, maintenance and optimization is still low, and demand-oriented visualization means are still insufficient. This paper designs a mobile network data analysis and visualization system. The designed system aims at addressing the problems that mobile network has various types of data. The designed system can make data easy to manage, improve the data analysis efficiency and the flexibility of data visualization for telecom operators.

This paper mines user behavior characteristics based on big data technology. This paper proposes a method for behavior slicing based on historical activity data, and insights into the personalized behavior characteristics. Firstly, the data is processed, and the classification is expanded on the basis of the parsed APP label types. Secondly, a time slicing method is proposed to reduce information loss, which integrates time, location, business type, and behavior into individual users. Then, based on slices of a day and a week, the paper analyzes user behavior and construct a portrait of user’s interest and preference. Finally, the periodic factor method is utilized to predict the behavior changes, forming the feature labels for users. Based on real business behaviors, this paper provides insight into user personality and effectively improves the authenticity and accuracy of prediction.

Recently, location-based service has become a hot research topic. Mobile communication data records abundant information about users’ temporal and spatial characteristics. By modeling the users’ mobility based on mobile communication data, this can assist to understand human user patterns more accurately and deeply. Initially, this paper introduces three mainstream algorithms for user mobility modeling. Then this paper proposes a novel Markov chain based user mobility prediction scheme. The proposed scheme is implemented through four stages, including time and space division, Markov property examination, transition probability matrix calculation, Markov model weighting. Experimental results show that the proposed scheme can achieve higher accuracy compared with the traditional algorithms.

Enterprises loss is a growth strategy, in which enterprises migrate across regions/cities to adapt to the changes of internal and external environment, in this way to seek new development space and further reach the growth again. As the carrier of local economic development, the transfer of enterprises from one region to another undoubtedly means the loss of regional resources for the region. This paper takes large- scale enterprises as the research object. Then, this paper uses questionnaire data and statistical data, and adopts the combination of PCA algorithm and extreme value standardization method to comprehensively evaluate the loss probability of enterprises. This method will reflect the loss tendency of enterprises in the region, and make an empirical analysis on the large-scale enterprises in region, in this way to help regional managers have an early insight into the loss tendency of enterprises in the region. Finally, it will provide a reference for stabilizing the regional economy and help reduce the loss risk of large-scale enterprises in the region.

With the rapid development of Internet industry, people are facing increasing challenge of information overload. Under this background, personalized recommendation has been comprehensively researched in order to provide a more time-saving and accurate way for information retrieval. In this paper, a novel hybrid recommendation scheme based on collaborative filtering and association rules is put forward to compensate the weaknesses of individual algorithms. This scheme is implemented through several steps. Firstly, it solves the problem of data sparsity with the help to association rules, and then employs the revised collaborative filtering to calculate the similarity among the items. Finally, it predicts user ratings for the unknown items based on item similarity and generates recommendation lists according to the prediction ratings. Experimental results show that the recommendation accuracy of this hybrid scheme has been dramatically improved compared to other traditional algorithms.

Distance measure is an essential tool to characterize the difference between two samples. Recently, lots of distance measures have been proposed for hesitant fuzzy sets (HFSs). In this paper, we shall propose some novel distance formulas to measure the deviation between two HFSs. First, we define some new concepts including the hesitant fuzzy variance, covariance, and correlation coefficient. Based on these concepts and the idea of the traditional Mahalanobis distance, the hesitant Mahalanobis distance between two HFSs is developed. Then we discuss the properties of the new distance measure and uncover the significant characteristic of the introduced distance measure that it can give the attributes an adaptive weight and can eliminate the influence of the correlation between the attributes under hesitant fuzzy environment. And then, some extensions of this new distance measure are also developed. Second, to show the validity and applicability of the proposed distance measures, we compare them with the existing ones in decision making and cluster analysis with some numerical examples. Third, using the proposed distance measures, we develop two algorithms to estimate the optimal number of clusters, which is a new application area of the hesitant fuzzy distance measures. Finally, the two algorithms are applied in a numerical example to illustrate their applicability and efficiency.

Traffic shifting is an efficient load balancing method to offload traffic from a hot-spot cell to neighbouring cells. This paper proposes a channel-aware optimised traffic shifting (COTS) scheme in LTE-Advanced relay networks. The COTS scheme employs a channel-aware assistant cell selection mechanism, which considers users' channel condition, received from the relay station (RS) in neighbouring cells, to select assistant cells and address the weak assistant cell problem. The optimal traffic offloading algorithm analyses and calculates the shifted traffic from the hot-spot cell to its assistant cells. Simulation results show that the COTS scheme can select a small number of neighbouring cells as assistant cells to effectively offload users, and can efficiently reduce the call blocking probability as well as the call dropping probability.