Mobile Demand Forecasting via Deep Graph-Sequence Spatiotemporal Modeling in Cellular Networks

Fang, Luoyang; Cheng, Xiang; Wang, Haonan; Yang, Liuqing

doi:10.1109/jiot.2018.2832071

Cited by 62 publications

(32 citation statements)

References 24 publications

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“…The traffic prediction can be very effective for smart network management and cut down the vagueness in supply and demand of network resources. The traffic prediction can also be helpful in reducing the capital and operational expenditures, accommodating the upcoming traffic variation with less resources [15], [16]. The precise traffic anticipation is indispensable for network planning optimization in both scenarios long and short term [17]- [19].…”

Section: A Forecasting Network Activities For Better Network Managementmentioning

confidence: 99%

“…In summary, there are number of network traffic modeling and prediction techniques have been established to cater the rising requirements of information communication technology (ICT) industry. These techniques are involve in many disciplines such as traffic prediction [15], [29] congestion control [30], [31], admission control [32], Network planning and optimization [33], [34], [19], [35], network management [36]- [38], improving the QoS for users [15], [36], [39], power management system [28] and traffic pattern analysis [40], [41].…”

Section: A Forecasting Network Activities For Better Network Managementmentioning

confidence: 99%

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Capacity and Frequency Optimization of Wireless Backhaul Network Using Traffic Forecasting

et al. 2020

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Telecom operators are aiming to provide high-grade data, multimedia applications and low latency videos for smart devices. As today's mobile data is experiencing rapid growth and the usage of smart devices are fabricating unparalleled challenges for telecom operators to meet the global bandwidth requirement. From the first generation to fourth generation, the technology evolution is predominantly governed by the hardware side but now it is moving towards the concept of cognitive network management, resource orchestration and machine learning-based solutions. In this paper, we propose the adaptive capacity and frequency optimization (ACFO) method for adaptive optimization based on time series forecasting approach. The daily capacity utilization of microwave (MW) links is analyzed to use forecasted demand. Based on the projected demand, the capacity and frequency optimization will be executed. The two main forecasting models 1) SARIMA and 2) MLP are used and for performance evaluation, we used RMSE and MAPE criterion. The analytic outcomes show that MLP with two layers and six hidden nodes (6/6) are good enough to achieve the desired results. In some cases, we need to exceed the hidden nodes up to fifteen (15/15). By using the forecasting approach, the reactive optimization will successfully shift to the predicted/ proactive optimization, will balance the resource distribution and can condense the wastage of resources. The outcome of the study will be a contribution to the dynamic resource optimization in wireless backhaul network. INDEX TERMS Time series forecasting, capacity planning, dynamic resource optimization, wireless backhaul optimization.

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Section: A Forecasting Network Activities For Better Network Managementmentioning

confidence: 99%

Section: A Forecasting Network Activities For Better Network Managementmentioning

confidence: 99%

Capacity and Frequency Optimization of Wireless Backhaul Network Using Traffic Forecasting

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Several IoT‐based forecasting solutions have been proposed in different areas, such as IoT‐systems based on air pollution monitoring and forecast systems [2], IoT‐based environmental monitoring using Raspberry Pi [3], IoT‐based electric load forecasting for smart grids [4], IoT‐based traffic demand forecasting in cellular networks [5], and IoT‐based short‐term weather prediction [6]. Ionospheric weather forecasts rely deeply on the ability to predict space weather events and are increasingly needed for positioning, satellite navigation and communication applications.…”

Section: Introductionmentioning

confidence: 99%

Implementation of IoT analytics ionospheric forecasting system based on machine learning and ThingSpeak

Dabbakuti

Jacob

Veeravalli³

et al. 2020

IET Radar, Sonar & Navigation

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“…The mobile traffic prediction schemes at aggregate level are based on time series prediction models, for instance, seasonal auto regression integrated moving average (ARIMA) in [2], and machine learning algorithms, like neural networks (NN), Gaussian process (GP) in [3,4]. Recently, more sophisticated methods like deep learning based approaches are employed to predict network traffic in [5]. The study of aggregate traffic can provide useful information for self-organization of the cellular network, for example, congestion control and energy saving based on the prediction results of the base station traffic [4,6,7].…”

Section: Introductionmentioning

confidence: 99%

Prophet model and Gaussian process regression based user traffic prediction in wireless networks

Pan

et al. 2020

Sci. China Inf. Sci.

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User traffic prediction is an important topic for wireless network operators. A user traffic prediction method based on Prophet and Gaussian process regression is proposed in this paper. The proposed method first employs discrete wavelet transform to decompose the user traffic time series to high-frequency component and low-frequency component. The low-frequency component bears the long-range dependence of user network traffic, while the high-frequency component reveals the gusty and irregular fluctuations of user network traffic. Then Prophet model and Gaussian process regression are applied to predict the two components respectively based on the characteristics of the two components. Experimental results demonstrate that the proposed model outperforms the existing time series prediction method.

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Mobile Demand Forecasting via Deep Graph-Sequence Spatiotemporal Modeling in Cellular Networks

Cited by 62 publications

References 24 publications

Capacity and Frequency Optimization of Wireless Backhaul Network Using Traffic Forecasting

Capacity and Frequency Optimization of Wireless Backhaul Network Using Traffic Forecasting

Implementation of IoT analytics ionospheric forecasting system based on machine learning and ThingSpeak

Prophet model and Gaussian process regression based user traffic prediction in wireless networks

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