## 2 Related Work  
### Abnormal Wireless Communication Performance Detection  
Accurate detection of abnormal wireless communication performance is the foundation of wireless network operation and maintenance. Presently, many detection methods have been proposed to solve this problem, which can be divided into two types: shallow model methods [4; 5; 6; 7] and deep model methods [8; 9; 10; 11; 12]. For example, [PERSON] et al. [4] analyzed the event sequences reported by mobile terminals to base stations and utilized the N-gram analysis method to reduce dimensionality and classify the data, ultimately achieving the detection of abnormal wireless communication performance. [PERSON] et al. [5] also analyzed the event sequences reported by mobile terminals to base stations and compared the accuracy of multiple anomaly detection methods for detecting abnormal wireless communication performance caused by random access channel anomalies. [PERSON] et al. [6] achieved high detection accuracy by using the kernel density-based local outlier factor (LOF) algorithm to detect cells with degraded communication performance. [PERSON] et al. [7] designed a decentralized abnormal wireless communication performance detection method based on the local outlier factor algorithm to perform abnormal detection in wireless sensor networks (WSNs). The above studies all used traditional shallow model machine learning methods to detect abnormal wireless communication performance. However, the development of neural networks, especially deep learning technologies, has provided a new research direction for the detection of abnormal wireless communication performance. [PERSON] et al. [8] proposed a deep learning-based lightweight distributed denial of service attack detection scheme to detect the anomaly in the data forwarding phase of WSN. [PERSON] et al. [9] compared the performance of the convex hull algorithm, naive Bayes algorithm, and CNN in the detection of abnormal nodes in WSN, and proved that CNN has the best detection performance. [PERSON] et al. [10] proposed a framework based on a long short-term memory (LSTM) deep learning technique that detects DDoS attacks in a 5G network. [PERSON] et al. [14] proposed an unsupervised spectrum anomaly detection method based on the variational autoencoder (VAE) and aimed to detect anomalies in unauthorized frequency bands in a wireless communication network. [PERSON] et al. [12] proposed a unique DDoS attack detection model, which combined the LSTM with the recurrent neural network (RNN) to construct the detection model, and utilized the opposition learning-based seagull optimization algorithm (OLSOA) to optimize the weight of the RNN for more accurate detection of DDoS attacks. [PERSON] et al. [3] proposed a dual encoder denoising autoencoder (DEDAE) neural network based on the non-dominated sorting genetic algorithm-III (NSGA-III) and generative adversarial network (GAN), which can detect LTE-R cells with degraded wireless communication performance.  
Since wireless communication network performance degradation is the result of many factors [2], the detection of abnormal wireless communication performance is usually a complex nonlinear problem. This paper chooses the deep model method to perform the detection of LTE-R cells with degraded communication performance.