Implementation of ARIMA model to predict Rain Attenuation for KU-band 12 Ghz Frequency

Patel, Dhaval; Patel, Mitul M.; Patel, Devendra R.

doi:10.9790/2834-09138387

Cited by 12 publications

(3 citation statements)

References 8 publications

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“…Another statistical approach for prediction algorithm is the ARIMA model and is used for Non-stationary time series applications. Such a model was discussed in the papers [13][14][15]. Reference [13] focused on the generation and prediction of time series values of only rain attenuation, whereas [14] described Box Jenkins time series seasonal ARIMA model for predicting rainfall on seasonal basis.…”

Section: Methodsmentioning

confidence: 99%

“…Such a model was discussed in the papers [13][14][15]. Reference [13] focused on the generation and prediction of time series values of only rain attenuation, whereas [14] described Box Jenkins time series seasonal ARIMA model for predicting rainfall on seasonal basis. Generally, ARIMA models are used for modelling hydrologic and geophysical time series data.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

2019

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Prediction of rainfall is one of the major concerns in the domain of meteorology. Several techniques have been formerly proposed to predict rainfall based on statistical analysis, machine learning and deep learning techniques. Prediction of time series data in meteorology can assist in decision-making processes carried out by organizations responsible for the prevention of disasters. This paper presents Intensified Long Short-Term Memory (Intensified LSTM) based Recurrent Neural Network (RNN) to predict rainfall. The neural network is trained and tested using a standard dataset of rainfall. The trained network will produce predicted attribute of rainfall. The parameters considered for the evaluation of the performance and the efficiency of the proposed rainfall prediction model are Root Mean Square Error (RMSE), accuracy, number of epochs, loss, and learning rate of the network. The results obtained are compared with Holt–Winters, Extreme Learning Machine (ELM), Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network and Long Short-Term Memory models in order to exemplify the improvement in the ability to predict rainfall.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

2019

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“…, ∀h; and (iii) the ARIMA model, which is a well-known time series prediction model. For an example of the ARIMA model being employed to predict rain-induced attenuation in Ku-band satellite links, we refer the reader to [39]. It is important to emphasize that both benchmark methods (i.e., naive and ARIMA) consider each link in isolation when predicting future attenuation levels and, thus, they do not capture the spatial correlation that is typical of weather-induced attenuation.…”

Section: Let Ementioning

confidence: 99%

Switching in the Rain: Predictive Wireless x-haul Network Reconfiguration

Kadota¹,

Jacoby²,

Messer³

et al. 2022

Preprint

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Wireless x-haul networks rely on microwave and millimeter-wave links between 4G and/or 5G base-stations to support ultra-high data rate and ultra-low latency. A major challenge associated with these high frequency links is their susceptibility to weather conditions. In particular, precipitation may cause severe signal attenuation, which significantly degrades the network performance. In this paper, we develop a Predictive Network Reconfiguration (PNR) framework that uses historical data to predict the future condition of each link and then prepares the network ahead of time for imminent disturbances.The PNR framework has two components: (i) an Attenuation Prediction (AP) mechanism; and (ii) a Multi-Step Network Reconfiguration (MSNR) algorithm. The AP mechanism employs an encoderdecoder Long Short-Term Memory (LSTM) model to predict the sequence of future attenuation levels of each link. The MSNR algorithm leverages these predictions to dynamically optimize routing and admission control decisions aiming to maximize network utilization, while preserving max-min fairness among the base-stations sharing the network and preventing transient congestion that may be caused by re-routing. We train, validate, and evaluate the PNR framework using a dataset containing over 2 million measurements collected from a real-world city-scale backhaul network. The results show that the framework: (i) predicts attenuation with high accuracy, with an RMSE of less than 0.4 dB for a prediction horizon of 50 seconds; and (ii) can improve the instantaneous network utilization by more than 200% when compared to reactive network reconfiguration algorithms that cannot leverage information about future disturbances

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Rain Attenuation

2022

Ground Station Design and Analysis for LEO Satellites

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Implementation of ARIMA model to predict Rain Attenuation for KU-band 12 Ghz Frequency

Cited by 12 publications

References 8 publications

Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

Switching in the Rain: Predictive Wireless x-haul Network Reconfiguration

Rain Attenuation

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