tuzsutТруды учебных заведений связиProceedings of Telecommunication Universities1813-324X2712-8830СПбГУТ10.31854/1813-324X-2023-9-3-14-27tuzsut-476Research ArticleЭЛЕКТРОНИКА, ФОТОНИКА, ПРИБОРОСТРОЕНИЕ И СВЯЗЬELECTRONICS, PHOTONICS, INSTRUMENTATION AND COMMUNICATIONSГрафовые нейронные сети для классификации трафика в каналах спутниковой связи: сравнительный анализGraph Neural Networks for Traffic Classification in Satellite Communication Channels: A Comparative Analysishttps://orcid.org/0000-0003-0645-0021ДоФ. Х.DoP. H.

аспирант кафедры программной инженерии и вычислительной техники Санкт-Петербургского государственного университета телекоммуникаций им. проф. Бонч-Бруевича

haodp@dau.edu.vnhttps://orcid.org/0000-0003-3735-0314ЛеЧ. Д.LeT. D.

кандидат технических наук, преподаватель кафедры сетей и коммуникаций Университета науки и технологий ‒ Университет Дананга

letranduc@dut.udn.vnhttps://orcid.org/0000-0002-1748-8642БерёзкинА. А.BerezkinA.

кандидат технических наук, доцент кафедры программной инженерии и вычислительной техники Санкт-Петербургского государственного университета телекоммуникаций им. проф. Бонч-Бруевича

berezkin.aa@sut.ruhttps://orcid.org/0000-0002-8781-6840КиричекР. В.KirichekR.

доктор технических наук, доцент, ректор Санкт-Петербургского государственного университета телекоммуникаций им. проф. М.А. Бонч-Бруевича

kirichek@sut.ruСанкт-Петербургский государственный университет телекоммуникаций им. проф. М.А. Бонч-БруевичаРоссияThe Bonch-Bruevich Saint-Petersburg State University of TelecommunicationsRussian FederationУниверситет науки и технологий – Университет ДанангаРоссияUniversity of Science and Technology – The University of DanangRussian Federation202310072023931427Copyright © До Ф.Х., Ле Ч.Д., Берёзкин А.А., Киричек Р.В., 20232023До Ф.Х., Ле Ч.Д., Берёзкин А.А., Киричек Р.В.Do P.H., Le T.D., Berezkin A., Kirichek R.This work is licensed under a Creative Commons Attribution 4.0 License.https://tuzs.sut.ru/jour/article/view/476

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.

This paper presents a comprehensive comparison of graph neural networks, specifically Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT), for traffic classification in satellite communication channels. The performance of these GNN-based methods is benchmarked against traditional Multi-Layer Perceptron (MLP) algorithms. The results indicate that GNNs demonstrate superior accuracy and efficiency compared to MLPs, emphasizing their potential for application in satellite communication systems. Moreover, the study investigates the impact of various factors on GNN algorithm performance, providing insights into the most effective strategies for implementing GNNs in traffic classification tasks. This research offers valuable knowledge on the benefits and prospective use cases of GNNs within satellite communication systems.

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The authors declare that there are no conflicts of interest present.