This paper focuses on the precise identification of potential cloud computer users utilizing telecom big data. By integrating multi-source heterogeneous data and leveraging key technical capabilities, including raw bitstream network data parsing and the construction of an innovative O-domain hierarchical tagging architecture, we develop a multidimensional user behavior profiling model. This model enables accurate identification of cloud computer usage patterns for both enterprise and individual users. Key innovations include: (1) a novel O-domain tagging framework that substantially enhances scenario classification accuracy; (2) an attentivestacking fusion model that dynamically prioritizes telecomspecific behavioral features to optimize prediction performance; and (3) a validated analysis plan demonstrating superior conversion outcomes and reduced operational costs in real-world deployments. The proposed attentive-stacking fusion model, trained on behavioral characteristics unique to telecom scenarios, significantly enhances the prediction accuracy of potential cloud computer user groups. Comparative experiments confirm that the analysis plan efficiently identifies high-conversion-potential cloud computer user segments, effectively addressing the challenge of high customer acquisition costs inherent in traditional marketing methods. This research establishes a novel pathway for telecom operators to leverage big data for targeted user mining, marketing expenditure reduction, and conversion efficiency improvement.

Contactless fingerprint has gained lots of attention in recent fingerprint studies. However, most existing contactless fingerprint algorithms treat contactless fingerprints as 2D plain fingerprints, and still utilize traditional contact-based 2D fingerprints recognition methods. This recognition approach lacks consideration of the modality difference between contactless and contact fingerprints, especially the intrinsic 3D features in contactless fingerprints. This paper proposes a novel contactless fingerprint recognition algorithm that captures the revealed 3D feature of contactless fingerprints rather than the plain 2D feature. The proposed method first recovers 3D features from the input contactless fingerprint, including the 3D shape model and 3D fingerprint feature (minutiae, orientation, etc.). Then, a novel 3D graph matching method is proposed according to the extracted 3D feature. Additionally, the proposed method is able to perform robust 3D feature extractions on various contactless fingerprints across multiple finger poses. The results of the experiments on contactless fingerprint databases show that the proposed method successfully improves the matching accuracy of contactless fingerprints. Exceptionally, our method performs stably across multiple poses of contactless fingerprints due to 3D embeddings, which is a great advantage compared to 2D-based previous contactless fingerprint recognition algorithms.

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.

Massive MIMO (Multiple Input and Multiple Output) is a key technology for improving 5G (the 5thGeneration) system capacity and spectrum utilization. This paper introduces the basic principles of Massive MIMO. Then, this paper proposes a novel LSTM-PD (LSTM and Periodic Decision) algorithm. The proposed LSTM-PD algorithm belongs to the category of periodic decision method with predictive properties. In addition, we also design a monitoring exit mechanism to improve the entire algorithm. The current network data results show that when the physical resource block (PRB) utilization rate is greater than 30%, the spectral efficiency of the LSTM-PD algorithm is significantly higher than that of the traditional algorithm. In addition, when the PRB utilization reaches 60%, the CPU utilization of the LSTM-PD algorithm can be reduced by nearly 30%, compared with the traditional algorithm.

Recently, information and digital technology are widely used in thousands of industries, leading to intelligent transformation, traditional methods, which lacks intelligent instrument. The safety of college students has attracted widespread attention from all walks of life, while campus safety management still adopts manual and traditional methods, which lacks intelligent instrument and big data resources and technologies are not fully utilized. In this paper, we propose a system of campus safety management based on telecom big data and data fusion architecture, providing solutions for intelligent campus management. In addition, a prediction algorithm of student behavior intent considering time spans has been proposed, proving the advantages in accuracy metrics and F1-score compared with traditional prediction algorithms.

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.

With the trend of 5G-A (5G-Advanced) harmonized network communications and sensing harmonized communication and sensing, network capability exposure technology will help operators, business providers and 3rd business parties to realize harmonized network communications and sensing harmonized communication and sensing business and applications. This paper will focus on capability exposure technology based on 5G-A harmonized communication and sensing. Initially, this paper discusses the capability exposure hierarchical architecture based on harmonized communication and sensing, secondly proposes the harmonized communication and sensing network architecture and basic network signaling process combined with capability exposure technology. Then, this paper discusses capability exposure application scenarios based on communication sensing. This paper can provide relevant reference for the technological evolution, network deployment and application discussion of the harmonized communication and sensing capability in the operator network.

Social network marketing is a very promising topic in the data operation work of telecom operators. Based on the big data collection and analysis of telecom operators, this paper presents a content recommendation scheme which considering both users' social relationships and users' personal preferences. Regarding users' personal preference analysis, this scheme uses DPI (Deep Packet Inspection) technology to obtain the user's personal preference tag and evaluate the user's preference index. In terms of user social relations, it integrates the analysis of mobile users' communication behaviors, temporal and spatial relationships, interaction circles and other related indicators. Logistic regression algorithm is used to illustrate the influence from a user to another. The preference recommendation scheme based on the mobile network user social circle proposed in this paper expands the value scenarios of operators' big data, integrates resources and channels, improves operators' data insight capabilities, and realizes the value mining and enhancement of operators' big data.

Psychological health of students has become a widespread social problem, while the management and assessment of college students' psychological health is still stay in passive and manual mode based on the traditional method. In this paper, we design a novel architecture for the prediction of college students' psychological health based on Multi-Source big data including Operation Support System big data, educational data and psychological health questionnaire data. Then we propose the Optimized Decision Tree using Multiple-Target Particle Swarm Optimization (DT-MTPSO) algorithm. Experiment shows that the proposed algorithm can solve the Multiple-Target problems effectively and has better performance in F1-score than traditional Decision Tree. In addition, the result of the features selection of DT-MTPSO for different targets shows the relationship between the psychological health level and behavioural characteristics of students for different evaluation indicators, providing guidance to the school managers and educational psychologist.

Data expansion is one of the commonly used steps in big data analysis applications. This paper proposes a data expansion method, which is based on operator data and considers multiple scenarios, multiple operating systems, and multiple operators in the target area. Factors such as the proportion of share and the difference in the proportion of users in the consumption power portrait are comprehensively expanded to obtain the full amount of user data of each target group in the target area. This method can be prepared to reflect changes in user data in time, and is applied to industries such as scene-based marketing and business planning.

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.

This paper studies optimal resource allocation for multiple network-coded two-way relay in orthogonal frequency division multiplexing systems. All the two-way relay nodes adopt amplify-and-forward and operate with analog network coding protocol. A joint optimization problem considering power allocation, relay selection, and subcarrier pairing to maximize the sum capacity under individual power constraints at each transmitter or total network power constraint is first formulated. By applying dual method, we provide a unified optimization framework to solve this problem. With this framework, we further propose three low-complexity suboptimal algorithms. The complexity of the proposed optimal resource allocation ORA algorithm and three suboptimal algorithms are analyzed, and it is shown that the complexity of ORA is only a polynomial function of the number of subcarriers and relay nodes under both individual and total power constraints. Simulation results demonstrate that the proposed ORA scheme yields substantial performance improvement over a baseline scheme, and suboptimal algorithms can achieve a trade-off between performance and complexity. The results also indicate that with the same total network transmit power, the performance of ORA under total power constraint can outperform that under individual power constraints. Copyright © 2012 John Wiley & Sons, Ltd.