Графовые нейронные сети для классификации трафика в каналах спутниковой связи: сравнительный анализ

В данной статье представлено всестороннее сравнение графовых нейронных сетей (GNN), в частности ‒ графовых сверточных сетей (GCN) и сетей внимания к графам (GAT), для классификации трафика в спутниковых коммуникационных каналах. Производительность этих методов, основанных на GNN, сравнивается с традиционными алгоритмами многослойного персептрона (MLP). Результаты показывают, что GNN обладают превосходной точностью и эффективностью по сравнению с MLP, что подчеркивает их потенциал для применения в системах спутниковой связи. Кроме того, в рамках исследования изучается влияние различных факторов на производительность алгоритма GNN, предоставляя информацию о наиболее эффективных стратегиях реализации GNN в задачах классификации трафика. Это исследование предлагает ценные знания о преимуществах и потенциальных применениях GNN в системах спутниковой связи.

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