Traffic forecasting in cellular networks using the LSTM RNN

Dalgkitsis, Anestis; Louta, Malamati; Karetsos, George T.

doi:10.1145/3291533.3291540

Cited by 31 publications

(20 citation statements)

References 12 publications

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“…A forecasting example that uses neural networks for delay minimization is already provided for FiWi networks in [48]. By taking the optimal training time and forecast accuracy trade-off into account, different neural networks approaches can also be used to detect patterns and forecast the network traffic characteristics, such as the traffic forecasting with long-short term memory method in [73].…”

Section: Discussionmentioning

confidence: 99%

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

2021

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Three convergent processes are likely to shape the future of the internet beyond-5G: The convergence of optical and millimeter wave radio networks to boost mobile internet capacity, the convergence of machine learning solutions and communication technologies, and the convergence of virtualized and programmable network management mechanisms towards fully integrated autonomic network resource management. The integration of network virtualization technologies creates the incentive to customize and dynamically manage the resources of a network, making network functions, and storage capabilities at the edge key resources similar to the available bandwidth in network communication channels. Aiming to understand the relationship between resource management, virtualization, and the dense 5G access and fronthaul with an emphasis on converged radio and optical communications, this article presents a review of how resource management solutions have dealt with optimizing millimeter wave radio and optical resources from an autonomic network management perspective. A research agenda is also proposed by identifying current state-of-the-art solutions and the need to shift all the convergent issues towards building an advanced resource management mechanism for beyond-5G.

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Section: Discussionmentioning

confidence: 99%

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

2021

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“…In this way, based on time series data, an LSTM is well‐suited to make predictions. Moreover, relative insensitivity to gap length is an advantage of LSTM over alternative RNNs, hidden Markov models, and other sequence learning methods [3].…”

Section: Deep Time Series Forecasting Strategymentioning

confidence: 99%

“…During the past years, the use of renewable energy sources (RESs) has increased, mainly due to the growing prices of conventional energy supplies. Considering the actual panorama of energy generation, a reliable electrical distribution system became the primary requirement of the smart grid [2], especially because power fluctuations caused by RESs can affect the stability, frequency control, and reliability of the power system [3]. To maintain the electrical network in operation, it is necessary to identify faults and correct them before irreversible faults occur.…”

Section: Introductionmentioning

confidence: 99%

Fault detection in insulators based on ultrasonic signal processing using a hybrid deep learning technique

Stefenon

Freire

Meyer

et al. 2020

IET Science, Measurement &Amp; Technology

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Identifying problems in insulators is a task that requires the experience of the operator. Contaminated insulators generally do not represent a system failure, however, due to higher surface conductivity, they may suffer from electrical discharges and may result in irreversible failures. The identification of possible failures in inspections can help to forecast faults to improve reliability in the power grid. Based on this need, this article presents a study on fault prediction in distribution insulators, through a laboratory evaluation in a contaminated insulator, where 13.8 kV (root mean square) was applied considering an ultrasound detector connected to a computer for data set acquisition. In the sequence, a time series prediction, using a hybrid deep learning technique defined as wavelet long short-term memory (LSTM), was performed. The hybrid LSTM was proposed considering feature extraction through the wavelet energy coefficient. Finally, for a complete evaluation, deeper LSTM layers were included, and both the training method and the hardware configuration were evaluated. The wavelet LSTM algorithm showed interesting accuracy results when compared to classic prediction algorithms like the non-linear autoregressive exogenous model.

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“…In [13], authors compare the two different forecasting approaches. Other authors have focused on the capabilities of Neural Networks-based approaches in forecasting mobile traffic: in [14] Recurrent Neural Networks (RNN) are used to predict short/long-term samples of mobile data traffic, showing that RNNs outperform ARIMA based models. In [15] authors create Long-Short Term Memory (LSTM) Neural Networks to predict long-term traffic and show that they outperform ARIMA-based methods and simpler Neural Network approaches.…”

Section: Related Workmentioning

confidence: 99%

“…Parameters θ are estimated so that the training error is minimized. • Neural Networks (TSD-NN): Recently, neural networks have been used to solve traffic forecasting problems [13], [14], [15]. Here we use a basic version of a Neural Network with N inputs, a single hidden layer with 32 nodes, and hyper-parameters tuned as reported in Table I.…”

Section: A Time Series Distributionmentioning

confidence: 99%

Forecasting Mobile Cellular Traffic Sampled at Different Frequencies

Okic

Redondi

2019

2019 12th IFIP Wireless and Mobile Networking Conference (WMNC)

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We consider the problem of forecasting highfrequency sampled mobile cellular traffic starting from a lowerfrequency sampled time series. We use a dataset of real downlink/uplink traffic traces obtained from a mobile cellular network and apply different methodologies for performing forecasts at different sampling frequencies. Through extensive evaluation we show that such type of forecasting is possible and in some cases is also able to outperform forecast results obtained starting directly from the high frequency time series. The outcomes of this work can be used for several scenarios of cognitive networking, including prediction of data traffic requests in specific locations, as well as for data storage optimization and improvement of BBU clustering tasks.

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Traffic forecasting in cellular networks using the LSTM RNN

Cited by 31 publications

References 12 publications

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

Fault detection in insulators based on ultrasonic signal processing using a hybrid deep learning technique

Forecasting Mobile Cellular Traffic Sampled at Different Frequencies

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