[PDF][PDF] Research on Optimization of 4G-LTE Wireless Network Cells Anomaly Diagnosis Algorithm based on Multidimensional Time Series Data.

B Qian, C Ma, T Zhang - IoTBDS, 2021 - scitepress.org
With the continuous increase of network terminal equipment, the operation scenarios of 4G-
LTE wireless networks are becoming more and more complex. The traditional manual …

A multidimensional time series data on wireless network to improve the detection performance of unsupervised learning algorithms

C Paul, S Ranjith, RS Vijayashanthi… - … and Smart Electrical …, 2022 - ieeexplore.ieee.org
In recent years, only with developments of cellular networking technologies as well as the
implementation of 4G LTE networks, the skills and options provided to end-users had …

Semi-supervised learning based big data-driven anomaly detection in mobile wireless networks

B Hussain, Q Du, P Ren - China Communications, 2018 - ieeexplore.ieee.org
With rising capacity demand in mobile networks, the infrastructure is also becoming
increasingly denser and complex. This results in collection of larger amount of raw data (big …

Unsupervised TCN-AE-based outlier detection for time series with seasonality and trend for cellular networks

R Mo, Y Pei, NV Venkatarayalu… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Timely identification of outliers occurring in key performance indicators (KPIs) of mobile
cellular networks is crucial for prompt action to unexpected events. The KPIs of cellular …

Lstm recurrent neural network (rnn) for anomaly detection in cellular mobile networks

SMA Al Mamun, M Beyaz - … , MLN 2018, Paris, France, November 27–29 …, 2019 - Springer
Anomaly detection can show significant behavior changes in the cellular mobile network. It
can explain much important missing information and which can be monitored using …

Multivariate time series unsupervised anomaly detection and diagnosis in 5g networks

S Ghosh, V Kataria - 2020 - tdcommons.org
For a variety of reasons (including, for example, increasing cyber security threats, increased
network heterogeneity, the increased use of virtualization technologies, etc.) maintaining the …

An anomaly detection algorithm based on ensemble learning for 5G environment

L Lei, L Kou, X Zhan, J Zhang, Y Ren - Sensors, 2022 - mdpi.com
With the advent of the digital information age, new data services such as virtual reality,
industrial Internet, and cloud computing have proliferated in recent years. As a result, it …

GenAD: General unsupervised anomaly detection using multivariate time series for large‐scale wireless base stations

X Hua, L Zhu, S Zhang, Z Li, S Wang, C Deng… - Electronics …, 2023 - Wiley Online Library
The reliability of wireless base stations is essential to guarantee the user experiences in
wireless networks, thereby employing the anomaly detection on multivariate time series is …

An efficient correlation-aware anomaly detection framework in cellular network

H Nan, X Zhu, J Ma - China Communications, 2022 - ieeexplore.ieee.org
Nowadays, the fifth-generation (5G) mobile communication system has obtained prosperous
development and deployment, reshaping our daily lives. However, anomalies of cell …

A Resource Efficient Model Training Based on Uncertainty Comparison to Detect Performance Anomalies in 5G Networks

HJ Lee, YJ Cho, D Kwak, J Lee - IECON 2023-49th Annual …, 2023 - ieeexplore.ieee.org
This paper introduces an efficient model training method for anomaly detection of radio units
(RUs) in 5G cellular networks. The proposed method uses Bayesian neural network (BNN) …