Специализированная нейронная сеть для прогнозирования параметров широкополосных трансионосферных радиоканалов в составе интеллектуального сенсора
Authors: Иванов Д.В. , Кислицын А.А. , Конкин Н.А. , Елчанинова А.С. , Чернов А.А. 
Published: February 2025

Развит подход к синтезу специальной рекуррентной нейронной сети для задачи прогнозирования полосы когерентности трансионосферных радиоканалов. Создан алгоритм подготовки данных и обучения нейронной сети. Валидация разработанной нейросети выполнена на данных натурных экспериментов, проведённых с помощью пассивных радиосенсоров глобальных навигационных спутниковых систем при использовании фазовых и кодовых измерений. Экспериментально показана достигаемая высокая точность прогноза.
Introduction. Artificial intelligence (AI) stands as a pivotal technology in satellite communications and radio navigation, contributing significantly to enhancing user experiences and optimizing communication stability. The urgent need for reliable coherence band prediction methods to ensure stable radiocommunication over transionospheric radio channels underscores the importance of developing more accurate and faster approaches using artificial intelligence. The aim of the work is to develop methods for analyzing and predicting the dynamics of coherence bands of wideband transionospheric radio channels using artificial neural networks. Data preparation and neural network training algorithm. The processing of data and the training of neural network models were conducted in the Python programming language, utilizing the Keras framework and the Tensorflow library. Auxiliary operations were performed using the numpy and pandas packages. Findings. The study scrutinizes the potential of employing existing neural networks in forecasting tasks for radio communication systems, drawing conclusions about the applicability of recurrent neural networks using fully connected and LSTM layers. The development of the method involved intelligent analysis of data from passive radio sensors in the Global Navigation Satellite System (GNSS) network. An algorithm for data preparation and training a neural network model for predicting the coherence band has been devised with the use of Keras Tuner library. This algorithm optimizes hyperparameters of the neural network in a given space, enhancing accuracy during training and validation. Conclusion. The resulting architecture of the neural network, based on hyperparameter selection, includes: an input layer (9,1); 1 LSTM layer with 260 neurons; 2 LSTM layer with 184 neurons; 1 Dense layer with 184 neurons (SeLU); 2 Dense layer with 152 neurons (SeLU); 3 Dense layer with 152 neurons (SeLU); 4 Dense layer with 80 neurons (SeLU); 5 Dense layer with 32 neurons (SeLU); Output Dense layer with 1 neuron; Optimizer: Adam, learning rate = 0.001. With an average Mean Absolute Percentage Error (MAPE) metric of 3.3%, the accuracy of model training and validation was notable. Experimental tests of the predicted coherence band, based on the synthesized neural network structure, resulted in a final accuracy of 18.2 MHz for Mean Absolute Error (MAE), an R2 coefficient of determination of 0.89, and a Mean Absolute Percentage Error (MAPE) of 6%.