ST-DenNetFus: A New Deep Learning Approach for Network Demand Prediction

Assem, Haytham; Çağlayan, Bora; Buda, Teodora Sandra; O’Sullivan, Declan

doi:10.1007/978-3-030-10997-4_14

Cited by 11 publications

(7 citation statements)

References 26 publications

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“…A comprehensive survey on cellular traffic prediction schemes could be found in [25,26]. A deep learning-powered approach for prediction of overall network demand in each region of cities has been proposed in [27]. In [20,28], the spatial and temporal correlations of the cellular traffic in different time periods and neighboring cells, respectively, have been explored using neural networks in order to improve the accuracy of traffic prediction.…”

Section: A Cellular Traffic Predictionmentioning

confidence: 99%

Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks

Azari¹,

Salehi²,

Papapetrou³

et al. 2021

Preprint

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There is a lack of research on the analysis of peruser traffic in cellular networks, for deriving and following traffic-aware network management. In fact, the legacy design approach, in which resource provisioning and operation control are performed based on the cell-aggregated traffic scenarios, are not so energy-and cost-efficient and need to be substituted with user-centric predictive analysis of mobile network traffic and proactive network resource management. Here, we shed light on this problem by designing traffic prediction tools that utilize standard machine learning (ML) tools, including long shortterm memory (LSTM) and autoregressive integrated moving average (ARIMA) on top of per-user data. We present an expansive empirical evaluation of the designed solutions over a real network traffic dataset. Within this analysis, the impact of different parameters, such as the time granularity, the length of future predictions, and feature selection are investigated. As a potential application of these solutions, we present an ML-powered Discontinuous reception (DRX) scheme for energy saving. Towards this end, we leverage the derived ML models for dynamic DRX parameter adaptation to user traffic. The performance evaluation results demonstrate the superiority of LSTM over ARIMA in general, especially when the length of the training time series is high enough, and it is augmented by a wisely-selected set of features. Furthermore, the results show that adaptation of DRX parameters by online prediction of future traffic provides much more energy-saving at low latency cost in comparison with the legacy cell-wide DRX parameter adaptation.

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Section: A Cellular Traffic Predictionmentioning

confidence: 99%

Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks

Azari¹,

Salehi²,

Papapetrou³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…A comprehensive survey on cellular traffic prediction schemes, including convolutional and recurrent neural networks, could be found in [13,15]. A deep learning-powered approach for prediction of overall network demand in each region of cities has been proposed in [2]. In [18,27], the spatial and temporal correlations of the cellular traffic in different time periods and neighbouring cells, respectively, have been explored using neural networks in order to improve the accuracy of traffic prediction.…”

Section: Related Work and Research Gapmentioning

confidence: 99%

“…Then, given a matrix X m (t) and a binary indicator vector s, we define X s m (t) the submatrix of X m (t), such that all respective rows, for which s indicates a zero value, are removed. For example, let Then, X s m (t) = [1,2]. Now, the research question in Section 1 could be rewritten as:…”

Section: Related Work and Research Gapmentioning

confidence: 99%

Cellular Traffic Prediction and Classification: A Comparative Evaluation of LSTM and ARIMA

Azari

Papapetrou

Denic

et al. 2019

Lecture Notes in Computer Science

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Prediction of user traffic in cellular networks has attracted profound attention for improving resource utilization. In this paper, we study the problem of network traffic traffic prediction and classification by employing standard machine learning and statistical learning time series prediction methods, including long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA), respectively. We present an extensive experimental evaluation of the designed tools over a real network traffic dataset. Within this analysis, we explore the impact of different parameters to the effectiveness of the predictions. We further extend our analysis to the problem of network traffic classification and prediction of traffic bursts. The results, on the one hand, demonstrate superior performance of LSTM over ARIMA in general, especially when the length of the training time series is high enough, and it is augmented by a wisely-selected set of features. On the other hand, the results shed light on the circumstances in which, ARIMA performs close to the optimal with lower complexity.

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“…Artificial intelligence has been used for deep learning based on LSTM units [44][45][46]. In [47], a convolutional neural network was used for prediction and modelling traffic spatial dependencies, the same as the approach in [48]. As indicated in [49], deep learning schemes, such as LSTM [50], convolutional neural networks [51] and recurrent neural networks [46], have also been applied to coarser time resolutions (e.g., an hour) to extend the forecasting horizon to several days.…”

Section: Introductionmentioning

confidence: 99%

Cellular Traffic Prediction Based on an Intelligent Model

Alsaade

Al-Adhaileh

2021

Mobile Information Systems

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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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ST-DenNetFus: A New Deep Learning Approach for Network Demand Prediction

Cited by 11 publications

References 26 publications

Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks

Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks

Cellular Traffic Prediction and Classification: A Comparative Evaluation of LSTM and ARIMA

Cellular Traffic Prediction Based on an Intelligent Model

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