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.

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.

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.

This paper investigates on the remote interference problem in the TDD network and proposes an intelligent 5G Remote Interference Avoidance and Clustering Scheme (RIAC), on the basis of RIM-RS (remote interference management-reference signal) and clustering algorithm. This paper adopts the GBLA-DBSACN (the grid-based local adaptive DBSCAN) algorithm based on the traditional DBSCAN algorithm (Density—Based Spatial Clustering of Application with Noise) to improve the accuracy of interference base station (BS) clustering, which considers the dispersion of interference sources. This scheme helps to locate interference problems and potential sources through testing in the existing network quickly and effectively. By taking corresponding optimization means for these problems, network operators can effectively reduce the interference level in the target area and improve the quality of network construction.

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.

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.