A machine learning approach for predicting SINR

The paper proposes a method for assigning a modulation-code scheme by a base station scheduler on unmanned aerial vehicle, based on predicting the value of the signal-to-interference ratio on the equipment of a mobile user at the next time slot from a sequence of known values of this ratio in the past. Prediction is performed using machine learning; for this, a single-layer neural network was built and applied to solve a multi-parameter optimization problem using the stochastic gradient method. The trained neural network for the predicted value of the signal / interference ratio allows the scheduler to select the modulationcode scheme correctly, thereby ensuring the level of quality of data transmission in the radio channel required for the provision of the service.

Publisher
Российский университет дружбы народов (РУДН)
Language
English
Pages
333-338
Status
Published
Year
2022
Organizations
  • 1 RUDN University
  • 2 Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences (FRC CSC RAS)
Keywords
SiNR; machine learning; neural network
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Adou K.Y.B., Markova E.V., Gaidamaka Yu.V.
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Ivanova N.M.
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