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Cited by 5 publications
(1 citation statement)
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“…The proposed model is compared with state-of-the-art solutions using historical averages such as ARIMA and LSTM models, showing that, although the Root Mean Square Error (RMSE) and the Mean Square Error (MSE) increase with the period ahead of the prediction, the model can reduce the handover frequency. Different from the rest, LSTM is exploited in [154] for multi-task learning (also covering incremental learning) pursuing handover management, and initial Modulation Coding Scheme (MCS) selection in 5G networks with the ultimate aim of optimizing operational efficiency and autonomy in the RAN management. Similar works, such as ABRAHAM [153], can also be found in Wi-Fi networks.…”
Section: Playgrounds For Building and Testingmentioning
confidence: 99%
“…The proposed model is compared with state-of-the-art solutions using historical averages such as ARIMA and LSTM models, showing that, although the Root Mean Square Error (RMSE) and the Mean Square Error (MSE) increase with the period ahead of the prediction, the model can reduce the handover frequency. Different from the rest, LSTM is exploited in [154] for multi-task learning (also covering incremental learning) pursuing handover management, and initial Modulation Coding Scheme (MCS) selection in 5G networks with the ultimate aim of optimizing operational efficiency and autonomy in the RAN management. Similar works, such as ABRAHAM [153], can also be found in Wi-Fi networks.…”
Section: Playgrounds For Building and Testingmentioning
confidence: 99%