In intelligent transportation systems, the ability to make precise and efficient lane-changing overtaking decisions is essential for improving traffic flow, safety, and overall efficiency. However, the coexistence of both connected and non-connected vehicles, driven by the high cost of full deployment and the incomplete global adoption of standardized communication standards, has led to the emergence of Mixed Connected and Connectionless Vehicles (MCCV) scenarios. These scenarios complicate lane-changing overtaking decisions, as the unpredictability of connectionless vehicles, particularly in dense traffic, poses significant challenges and increases safety risks. Furthermore, incorporating fairness into decision-making is vital to ensure equitable treatment of all vehicles, which is key to improving road safety and traffic efficiency. To address these challenges, we propose a fairness-aware overtaking decision optimization method for MCCV scenarios, which aims to enhance fairness while improving safety and efficiency in vehicle decision-making. First, a Bayesian network-based fairness assessment method is introduced to quantify fairness under limited data conditions by modeling the probabilistic relationships between vehicle behaviors and fairness outcomes. Second, we develop a left-lane availability detection mechanism based on adaptive maneuver tree search and a fast-lane speed recommendation mechanism grounded in traffic flow analysis. These mechanisms enhance compliance with traffic regulations and improve efficiency by dynamically assessing lane conditions and providing a real-time speed recommendation based on traffic density and flow. Finally, we incorporate a K-Nearest Neighbor (KNN)-enhanced deep reinforcement learning approach, which integrates a parameterized dueling deep recurrent Q-network with KNN-enhanced experience replay. This approach effectively copes with rare but critical driving conditions, such as unpredictable vehicle behaviors or sudden traffic dynamics, improving decision-making reliability in various traffic scenarios. Extensive simulations demonstrate that the proposed method significantly enhances the fairness, efficiency, and safety of lane-changing overtaking decisions in MCCV scenarios, effectively addressing the unpredictability of mixed-vehicle interactions.

For the front-end, large-scale data acquisition is usually realized through paging request mechanism, the efficiency of this mechanism is limited by the browser mechanism, server processing capability and network conditions. In order to achieve efficient processing and display of a large number of geographic data nodes in the front-end big data visualization platform, the scheme proposes a data loading optimization mechanism based on paging request mechanism, which comprises three modules: a detector, a record library and a recommender. The detector is used to detect request conditions in specific environments, the record library stores the detection results, and the recommender returns appropriate paging request results. The scheme not only improves the data processing efficiency, but also enhances the user interaction experience. The scheme can be used in the field of data visualization in various industries, especially for the rapid visualization of geographic data.

With the rapid development in 6G networks, traditional network operation and maintenance (O&M) approaches are insufficient to handle the scale and real-time demands. This paper presents a novel O&M system, applying a multiagent scheduling algorithm to autonomously detect, diagnose, and resolve network faults. The system is structured in five layers: Data, Data Model, Agent, Application, and Interaction layers. And five classes of agents are integrated. Experimental results show the system's ability to outperform manual processes, which demonstrates the efficiency for the demanding needs of nextgeneration network management.

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.

Recently, numerous deep learning algorithms have addressed wearable human activity recognition (HAR), but they often struggle with efficient knowledge transfer to lightweight models for mobile devices. Knowledge distillation (KD) is a popular technique for model compression, transferring knowledge from a complex teacher to a compact student. Most existing KD algorithms consider homogeneous architectures, hindering performance in heterogeneous setups. This is an under-explored area in wearable HAR. To bridge this gap, we propose a heterogeneous mutual KD (HMKD) framework for wearable HAR. HMKD establishes mutual learning within the intermediate and output layers of both teacher and student models. To accommodate substantial structural differences between teacher and student, we employ a weighted ensemble feature approach to merge the features from their intermediate layers, enhancing knowledge exchange within them. Experimental results on the HAPT, WISDM, and UCI_HAR datasets show HMKD outperforms ten state-of-the-art KD algorithms in terms of classification accuracy. Notably, with ResNetLSTMaN as the teacher and MLP as the student, HMKD increases by 9.19% in MLP's $F_{1}$ score on the HAPT dataset.

The fragmented architecture, cloud-based infrastructure, and functionally virtualized network elements within the 5 G core network have significantly surged the volume and diversity of alarms generated on cloud network service platforms that it supports. Given the inherently cross-layered nature of failure scenarios on these platforms, identifying the root causes presents a significant challenge. Alarm association rule mining has become an effective means to address the problems of alarm correlation and root cause localization. In this paper, an explainable alarm association rule mining approach based on knowledge graph, referred to as ARK-G, is proposed. Initially, a cloud-network cross-layer alarm association knowledge graph (CA2KG) is constructed. Subsequently, the knowledge embedding based graph convolutional network is employed to perform knowledge graph embedding on CA2KG. This embedding is then utilized to enhance the RNNLogic algorithm, thereby facilitating cross-layer alarm association rule mining with interpretable paths. Finally, a weighted rule tree is derived from a subset of CA2KG and the generated explainable rules, enabling the deduction of the root alarm. Experimental results demonstrate that the proposed ARK-G approach for association rule mining yields a higher hit rate compared to the baseline model, which provides valuable assistance in the faults analysis of 5 G cloud-network platforms.

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.

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.

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.

With the improvement of the mobile communication market, the smart home business of telecom operators is showing unprecedented development and contribution. With the full-service competition in the domestic telecommunications industry, operators are facing increasing competitive pressure. The home service has become one of the important competitions for operators. In order to solve the user’s relationship recognition problem, this paper compares five commonly used machine learning methods: logistic regression, decision tree, random forest, LightGBM and multi-layer perceptron. Based the experiments on the data set, the results demonstrate the efficient of the LightGBM method. Finally, this paper selects the LightGBM method as the technical method for user relationship recognition in the business.

Visual Question Answering (VQA) is a recent hot topic that involves multimedia analysis, computer vision (CV), natural language processing (NLP), and even a broad perspective of artificial intelligence, which is challenging and has obtained increasing attention. VQA needs a complete understanding of the spatial relationship, textual clues, as well as the common sense for an actual image. However, most existing approaches simply embed and concatenate the features of questions and images to predict answers. Treating all embeddings equally without consideration of relation consistency hinders the model performance. In this paper, we propose an explicit Multi-Granularity Cross-Attention network (MGCAN) that mutually learns the multi-modal branches. MGCAN jointly matches word-level representation with whole image, and patch-level representation with the whole question that infers the high-order vision-semantic relationship. Experiments conducted on VQA datasets demonstrate that the proposed MGCAN outperforms previous baselines. The cross-attention mechanism explicitly exploits the relevant visual and textual clues that lead to superior prediction.

User address localization technologies have far-reaching implications for operators and governments. These technologies serve to optimize the planning and operation of communication networks and enhance the quality of network services, thereby providing powerful support for the provision of social public services, urban planning, and emergency management. The existing user address localization methods used by operators mainly rely on base stations locations and user-reported GPS. However, when the coverage area of a base station is too large, the localization accuracy will be poor even if triangulation techniques are used. Moreover, since the user-reported GPS is triggered by specific network events, its time continuity and volume are unstable. To address these limitations, this paper provides a user address localization method based on data fusion of signaling data from core network, measurement report (MR) data from radio access network, and operational data from carriers’ business support system (BSS). The feature engineering is performed from three dimensions, including base station level analysis, GPS level analysis, and address level analysis, to construct a building-level user address localization model. During data fusion, the signaling data compensates the weak time continuity of MR data, and the MR data compensates the poor localization accuracy of signaling data. In addition, through fusion with BSS data, it is able to convert users’ GPS positions into building-level address locations, thereby further improving the practicality and accuracy of the model..

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.

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.

Customer churn prediction is critical to an enterprise. Therefore, the improvement of the churn prediction model can directly help enterprises to better manage customers to obtain more profit. Meanwhile, the explanation of the decision-making mechanism of the churn prediction model can guide enterprises to accurately understand and evaluate the likelihood of customer churn, and then make more targeted measures to prevent customer churn. However, most existing deep learning approaches have poor model interpretability due to the black-box characteristics of neural networks. In addition, there are few previous studies on the model interpretability of customer churn predictions. Accordingly, in this paper, we propose a novel model named Interpretable DeepFM (iDeepFM) to study the model interpretability. The Proposed iDeepFM model introduces multi-head self-attention mechanism to the embedding layer, the linear interaction component, and the deep component. Especially, the deep component computes the high-order feature interactions by stacking multiple attention blocks. Furthermore, we conduct extensive experiments using two real-world telecom customer churn datasets. We calculate attention scores which are used to interpret the prediction results for each component. The results show that the proposed approach not only improves the performance of DeepFM but also offers good model interpretability.

In quantitative trading, stock prediction plays a crucial role in portfolio optimization as it directly impacts the actual level of return. However, the trading market is complex, making return prediction a challenging task. To address this issue, existing works have utilized various technical indicators as inputs to enhance predictive accuracy. However, these indicators are often proprietary and kept confidential by quantitative funds and researchers, limiting their accessibility. In this paper, we propose a federated learning-based method that leverages multiple parties’ technical indicators for stock return prediction without disclosing them. The results demonstrate that the proposed method outperforms traditional methods in terms of prediction accuracy. Additionally, the proposed method achieves higher portfolio return through portfolio optimization using the Mean-variance Optimization model compared to traditional approaches. The proposed method offers a promising solution for stock return prediction while maintaining the confidentiality of technical indicators.

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.

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.

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.

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.

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.

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.

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 disappearance of the demographic dividend and the saturation of the public telecom market, telecom operators need new development strategies urgently. New services formed by the convergence of mobile network services and broadband network services (referred to as fixed-mobile convergence services) have become an important strategy. Through business innovation, telecom operators can bundle mobile services with broadband services, which can enhance user stickiness and increase business revenue. Based on the joint analysis of mobile network data and broadband network data, this paper proposes a rule-based and model-based integrated method for mining potential fixed-mobile convergence target users. After applying this method to the real market for single mobile contract user transferring to convergent contract, results show that the proposed method for exploiting potential target users can increase the conversion rate of convergence users.

With the rapid progress of the telecom industry and fierce competition among telecom operators, telecom companies pay more attention to customer retention. Telecom companies developed multiple solutions to predict churn customers before customers move to another telecom operator. However, the existing prediction solutions have some disadvantages in the real-world use cases. For example, churn definition is limited to moving from one telecom operator to another, which is too late for preventing customer churn. The main contribution of the paper is to introduce the new definition of customer chum for the telecom industry, and to propose a Half Termination Dynamic Label (HTDL) that improves the churn prediction solution with XGBoost. Experiment results showed that the proposed solution improved the model performance, which significantly outperforms traditional solution, in terms of churn prediction on F1-score. The new solution also sidelines more active customers for retention.

In the 5G era, mobile networks gradually become complex, and there are also high requirements for network operation and maintenance. As log data is important information to reflect the status of network devices, the monitoring of log data generated by network devices has become an important part of network operation and maintenance. But the massive amount of log data generated by large-scale network devices has already exceeded the range of human processing capabilities. And the introduction of artificial intelligence algorithms can optimize the detection of log anomalies and reduce network operation and maintenance costs under the challenges of high complexity of 5G networks. This paper proposes a Mahalanobis distance and Pauta criterion based log anomaly detection (MPLAD) algorithm for 5G mobile network. On the basis of solving the shortcomings of the existing log anomaly detection algorithms, it innovatively integrates the Mahalanobis distance algorithm and the Pauta criterion. Meanwhile, it also introduces the negative sample mechanism and the principal component analysis (PCA) method to achieve high accuracy, high efficiency and high compatibility towards 5G mobile network log anomaly detection.

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.

Network slicing is a key technology in 5G. Generally, 5G networks employ slicing technology to provide the isolated and customizable network services for different scenarios (e.g., different vertical industries, different customers, different businesses etc.) in the form of virtual industry private networks. 5G slicing has the potential to meet the individual requirements of users and services, in terms of bandwidth, delay, reliability, and mobility. This paper gives an overall introduction to network slicing management, end-to-end processes, and wireless slicing capabilities. Then, this paper carries on algorithm research for wireless RB resource reservation and QoS scheduling. On one hand, the proposed algorithm clarifies the specific scheme of wireless RB resource reservation. On the other hand, the algorithm provides QoS scheduling parameter configuration. This lays a solid foundation for the implementation of slice differentiation capabilities in 5G wireless networks.

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.

The economic expenditure of mobile network operators includes two parts, namely CAPEX and OPEX. CAPEX mainly includes the huge amount of capital invested in network infrastructure construction, while operating expenditure mainly includes expenditure for daily operation and maintenance. In order to achieve continuous coverage of wireless network, CAPEX needed for base station procurement is indispensable. Operators need to adopt more intelligent and scaled means to optimize the maintenance process of wireless network so as to better achieve the goal of cost reduction and efficiency increase. In this paper, a scheme of cell boundary prediction and base station location information verification based on machine learning is proposed, which innovatively introduces the machine learning algorithm into network optimization analysis and improve the verification efficiency and reduce the input of manpower.

5G mobile edge computing (MEC) can be used in intelligent manufacturing. In complex industrial application scenarios, this paper tries to address the problem of energy consumption optimization of customer task unloading and resource rescheduling. Specifically, we employ 5G wireless private network and MEC computing resources between 5G private network and MEC. Then, we use game theory algorithm to optimize the user task unloading and resource rescheduling allocation, which is mainly measured by the minimum total time required to complete the task and energy consumption. The problem is a combinatorial nonlinear programming algorithm, involving joint optimization of task offloading decision, user side's uplink transmission energy consumption, MEC server's resource allocation. The solution includes the resource allocation of fixed task unloading decision, and the resource allocation optimization of task unloading. Results verify the proposed solution can improve the efficiency of task scheduling.

The stability of passive antenna feed operation is an important indicator to measure the quality of wireless network. On the basis of big data of antenna and feed fault, this paper proposes a support vector machine (SVM) based fault classifier of antenna and feed, in order to quickly classify the faults of antenna and feed system (AFS). In addition, the improved Cascaded Pyramid Network (CPN) learning algorithm is employed to establish a fault diagnosis device of antenna and feed to quickly diagnose various categories of faults. For the fault model of antenna and feed, we continue to learn and train to optimize the fault classifier model, as well as the fault diagnosis model. For the fault diagnosis information, the antenna and feed fault classifier is used to update the classified faults, which empower the antenna and feed fault classification more accurate.

5G network consumes huge investment cost, including 5G network construction, 5G network operation and maintenance etc. Therefore, China Unicom and China Telecom take the initiative to jointly build the 5G network (wireless network sharing, independent construction mode of core network). Its aim is to reduce 5G overall investment cost, and rapidly realize the continuous and wide-area 5G service capability, as well as improve the network efficiency and asset operation efficiency. This paper focuses on the automatic data configuration model of 5G co-construction and shared base stations. By interacting with the core network and wireless network, this model can identify and match different 5G network modes such as SA and NSA (including dual-anchor scenarios and single-anchor scenarios). On this basis, the data required for automatic activation of the shared base stations is obtained from the wireless side and automatically configured on the core network side. This technology meets the practical needs of both sides for fast and efficient construction of 5G network. It can realize the automatic opening and fast on-line of co-construction and shared BS on the side of the core network.

This paper studies the Internet characteristics of Internet media news based on three factors, including journalism, communication, statistical physics. By obtaining the indicators (e.g., news release time, title, text, media, media type, media weight, etc.), the news transmission speed evaluation system is constructed by employing clustering model, and the news transmission speed is further obtained. Through this indicator and system, it can effectively reflect the transmission speed and changing trend of news events, and monitor the spread situation of news events in real time. In addition, this paper provides a reference basis for the governance of network public opinion and the early warning as well as handling for crisis events.

EPS-Fallback is generally adopted for voice service in the coexistence period of 4/5G network. Its process is very complex and prone to failure. How to guarantee users perception is an important challenge. Based on multi-resources data, this paper proposes a fault diagnosis method for EPS-Fallback. It can effectively improve the fault diagnosis ability and the efficiency of network operation and maintenance. Also, it can save labor costs.

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.

This paper presents an improved decryption method aiming at covert Orthogonal Frequency Division Multiplexing (OFDM) transmission with cyclic delay diversity (CDD) featured multiple input multiple output (MIMO) technology. Particularly, we take advantage of the inherent sparse structure of multi-path wireless channel as well as Compressive Sensing (CS) technology, which has attracted a considerable attention for its breakthrough on Shannon's sampling theorem. Our work addresses the issues of bounded application range for conventional decryption methods, which is caused by the limitation of channel estimation with respect to uneven pilot arrangement that employed as a physical layer secret key. Besides, our proposed method relaxes minimum requirement for pilot number while the performance gain is improved instead. Simulation results show that our proposed method can achieve a considerable performance gain, with extra rewards of high spectral efficiency and much broader application prospect.