scholarly journals CELLULAR NETWORK TRAFFIC PREDICTION USING EXPONENTIAL SMOOTHING METHODS

2018 ◽  
Author(s):  
Quang Thanh Tran ◽  
Li Jun Hao ◽  
Quang Khai Trinh
Keyword(s):  
Network Traffic ◽  
Cellular Network ◽  
Low Cost ◽  
Real Data ◽  
Exponential Smoothing ◽  
High Accuracy ◽  
Training Data ◽  
Traffic Prediction ◽  
Data Traffic ◽  
Smoothing Methods

Wireless traffic prediction plays an important role in network planning and management, especially for real-time decision making and short-term prediction. Systems require high accuracy, low cost, and low computational complexity prediction methods. Although exponential smoothing is an effective method, there is a lack of use with cellular networks and research on data traffic. The accuracy and suitability of this method need to be evaluated using several types of traffic. Thus, this study introduces the application of exponential smoothing as a method of adaptive forecasting of cellular network traffic for cases of voice (in Erlang) and data (in megabytes or gigabytes). Simple and Error, Trend, Seasonal (ETS) methods are used for exponential smoothing. By investigating the effect of their smoothing factors in describing cellular network traffic, the accuracy of forecast using each method is evaluated. This research comprises a comprehensive analysis approach using multiple case study comparisons to determine the best fit model. Different exponential smoothing models are evaluated for various traffic types in different time scales. The experiments are implemented on real data from a commercial cellular network, which is divided into a training data part for modeling and test data part for forecasting comparison. This study found that ETS framework is not suitable for hourly voice traffic, but it provides nearly the same results with Holt–Winter’s multiplicative seasonal (HWMS) in both cases of daily voice and data traffic. HWMS is presumably encompassed by ETC framework and shows good results in all cases of traffic. Therefore, HWMS is recommended for cellular network traffic prediction due to its simplicity and high accuracy.  

scholarly journals Attention based multi-component spatiotemporal cross-domain neural network model for wireless cellular network traffic prediction

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Qingtian Zeng ◽  
Qiang Sun ◽  
Geng Chen ◽  
Hua Duan
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Traffic ◽  
Cellular Network ◽  
Critical Issue ◽  
Traffic Prediction ◽  
Weekly Cycle ◽  
Cross Domain ◽  
Cellular Traffic

AbstractWireless cellular traffic prediction is a critical issue for researchers and practitioners in the 5G/B5G field. However, it is very challenging since the wireless cellular traffic usually shows high nonlinearities and complex patterns. Most existing wireless cellular traffic prediction methods lack the abilities of modeling the dynamic spatial–temporal correlations of wireless cellular traffic data, thus cannot yield satisfactory prediction results. In order to improve the accuracy of 5G/B5G cellular network traffic prediction, an attention-based multi-component spatiotemporal cross-domain neural network model (att-MCSTCNet) is proposed, which uses Conv-LSTM or Conv-GRU for neighbor data, daily cycle data, and weekly cycle data modeling, and then assigns different weights to the three kinds of feature data through the attention layer, improves their feature extraction ability, and suppresses the feature information that interferes with the prediction time. Finally, the model is combined with timestamp feature embedding, multiple cross-domain data fusion, and jointly with other models to assist the model in traffic prediction. Experimental results show that compared with the existing models, the prediction performance of the proposed model is better. Among them, the RMSE performance of the att-MCSTCNet (Conv-LSTM) model on Sms, Call, and Internet datasets is improved by 13.70 ~ 54.96%, 10.50 ~ 28.15%, and 35.85 ~ 100.23%, respectively, compared with other existing models. The RMSE performance of the att-MCSTCNet (Conv-GRU) model on Sms, Call, and Internet datasets is about 14.56 ~ 55.82%, 12.24 ~ 29.89%, and 38.79 ~ 103.17% higher than other existing models, respectively.

scholarly journals Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network

Algorithms ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 20 ◽  
Author(s):  
Dehai Zhang ◽  
Linan Liu ◽  
Cheng Xie ◽  
Bing Yang ◽  
Qing Liu
Keyword(s):  
Deep Learning ◽  
Network Traffic ◽  
Cellular Network ◽  
Auxiliary Information ◽  
Machine Learning Algorithms ◽  
Traffic Prediction ◽  
Learning Method ◽  
Convolution Model ◽  
Communication Traffic ◽  
Cellular Traffic

With the arrival of 5G networks, cellular networks are moving in the direction of diversified, broadband, integrated, and intelligent networks. At the same time, the popularity of various smart terminals has led to an explosive growth in cellular traffic. Accurate network traffic prediction has become an important part of cellular network intelligence. In this context, this paper proposes a deep learning method for space-time modeling and prediction of cellular network communication traffic. First, we analyze the temporal and spatial characteristics of cellular network traffic from Telecom Italia. On this basis, we propose a hybrid spatiotemporal network (HSTNet), which is a deep learning method that uses convolutional neural networks to capture the spatiotemporal characteristics of communication traffic. This work adds deformable convolution to the convolution model to improve predictive performance. The time attribute is introduced as auxiliary information. An attention mechanism based on historical data for weight adjustment is proposed to improve the robustness of the module. We use the dataset of Telecom Italia to evaluate the performance of the proposed model. Experimental results show that compared with the existing statistics methods and machine learning algorithms, HSTNet significantly improved the prediction accuracy based on MAE and RMSE.

scholarly journals ECMCRR-MPDNL for Cellular Network Traffic Prediction With Big Data

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 113419-113428
Author(s):  
Venkata Subbaraju Dommaraju ◽  
Karthik Nathani ◽  
Usman Tariq ◽  
Fadi Al-Turjman ◽  
Suresh Kallam ◽  
...  
Keyword(s):  
Big Data ◽  
Network Traffic ◽  
Cellular Network ◽  
Traffic Prediction

Application on Network Traffic Prediction Based on Least Squares Support Vector Machine

2010 ◽  
Vol 20-23 ◽  
pp. 364-369 ◽  
Author(s):  
Yu Zhuo Ren ◽  
Ke Wen Xia ◽  
Yan Wang ◽  
Jun Shi
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Network Traffic ◽  
High Accuracy ◽  
Traffic Model ◽  
Traffic Prediction ◽  
Support Vector ◽  
Actual Application ◽  
Sample Data ◽  
Network Traffic Model

The network traffic is one of the important metrics for describing network behaviors, it plays an important role in network design, network protocol and traffic project implementation. In order to solve some problems in network traffic prediction, according to actual data for network- monitoring traffic, an approach to network traffic prediction is presented based on least squares support vector machine (LS-SVM), it mainly includes selecting for sample data of network traffic, normalization processing of data, network traffic model trained by LS-SVM and network traffic prediction, etc. Actual application results indicate that the method of network traffic prediction has high accuracy and good feasibility.

scholarly journals Cellular Traffic Prediction Based on an Intelligent Model

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Fawaz Waselallah Alsaade ◽  
Mosleh Hmoud Al-Adhaileh
Keyword(s):  
Network Traffic ◽  
Cellular Network ◽  
Short Term Memory ◽  
Intelligent System ◽  
Exponential Model ◽  
Exponential Smoothing ◽  
Superior Performance ◽  
Network Loading ◽  
Single Exponential ◽  
Cellular Traffic

The evolution of cellular technology development has led to explosive growth in cellular network traffic. Accurate time-series models to predict cellular mobile traffic have become very important for increasing the quality of service (QoS) with a network. The modelling and forecasting of cellular network loading play an important role in achieving the greatest favourable resource allocation by convenient bandwidth provisioning and simultaneously preserve the highest network utilization. The novelty of the proposed research is to develop a model that can help intelligently predict load traffic in a cellular network. In this paper, a model that combines single-exponential smoothing with long short-term memory (SES-LSTM) is proposed to predict cellular traffic. A min-max normalization model was used to scale the network loading. The single-exponential smoothing method was applied to adjust the volumes of network traffic, due to network traffic being very complex and having different forms. The output from a single-exponential model was processed by using an LSTM model to predict the network load. The intelligent system was evaluated by using real cellular network traffic that had been collected in a kaggle dataset. The results of the experiment revealed that the proposed method had superior accuracy, achieving R-square metric values of 88.21%, 92.20%, and 89.81% for three one-month time intervals, respectively. It was observed that the prediction values were very close to the observations. A comparison of the prediction results between the existing LSTM model and our proposed system is presented. The proposed system achieved superior performance for predicting cellular network traffic.

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