# Anomaly Detection and Early Warning Model for Latency in Private 5G Networks
###### Abstract  
Different from previous generations of communication technology, 5G has tailored several modes especially for industrial applications, such as Ultra-Reliable Low-Latency Communications (URLLC) and Massive Machine Type Communications (mMTC). The industrial private 5G networks require high performance of latency, bandwidth, and reliability, while the deployment environment is usually complicated, causing network problems difficult to identify. This poses a challenge to the operation and maintenance (O&M) of private 5G networks. It is needed to quickly diagnose or predict faults based on high-dimensional data of networks and services to reduce the impact of network faults on services. This paper proposes a ConvAE-Latency model for anomaly detection, which enhances the correlation between target indicators and hidden features by multi-target learning. Meanwhile, transfer learning is applied for anomaly prediction in the proposed LstmAE-TL model to solve the problem of unbalanced samples. Based on the China Telecom data platform, the proposed models are deployed and tested in an Automated Guided Vehicles (AGVs) application scenario. The results have been improved compared to existing research. p 2022  
location cycle and difficulty of root cause tracing seriously affect productivity and customer experience. In the face of these factors, there is an urgent need for efficient, rapid, accurate, and low-cost approaches to meet the growing demand for digital O&M. The concept of data-driven artificial intelligence for IT operations, intelligent operations, and maintenance (AIOps) [1] was presented in 2016 and has become a trending research direction, aiming to solve the low efficiency of O&M caused by inexperience. It combines traditional O&M with artificial intelligence algorithms, big data and artificial intelligence, and other methods to improve the network's O&M capabilities and reduce human interference in the O&M, with the ultimate goal of achieving unmanned and fully automated O&M. In 2018, the white paper \"Enterprise AIOps Implementation Recommendations\" was jointly initiated and developed by the Efficient Operations and Maintenance community and the AIOps Standard Working Group [2]. Based on the research work in [3], most existing intelligent O&M frameworks are based on machine learning methods, which mainly include the following three aspects: anomaly detection, anomaly localization, and abnormal early warning. Specific research is presented in the related work in Section 2. Most research in the field of anomaly monitoring determines whether an abnormal state exists at the current moment by calculating the error between the current and normal intervals. Based on association rule mining or decision trees, which are mature algorithms in anomaly localization, data of the top-ranked dimension can be returned by ranking the channel anomalous factors (or errors) within the anomaly duration or by reducing the search space complexity through algorithm design. However, existing anomaly localization studies mainly focus on research from the perspective of supervised classification, which is contrary to the background of most unlabeled data in the actual production environment. Abnormal early warning studies focus on regression prediction for time variable series or by matching the real-time data distribution with failure rules to obtain the probability of failure occurrence. After thorough research, it was unfortunately found that the progress of intelligent O&M research lies only in the field of scientific research, and there are still some difficulties in combining the algorithms of academia with the domain knowledge of the industry and applying them to actual industrial production scenarios.