Deep learning aided transmission power control for wireless sensor networks

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dc.contributor.authorLeblebici, Merih
dc.contributor.authorCalhan, Ali
dc.date.accessioned2026-07-01T11:40:11Z
dc.date.available2026-07-01T11:40:11Z
dc.date.issued2026
dc.departmentDüzce Üniversitesi
dc.description.abstractThis study presents a novel deep learning (DL)-based transmission power control (TPC) method for wireless sensor networks (WSN), focused on improving energy efficiency and network performance. By utilizing DL to estimate signal-to-noise ratio (SNR) for various modulation schemes, we enable the master sensor to optimize power consumption in connected sensors. The method involves training models for eight modulation schemes, including quadrature phase shift keying (QPSK), 8PSK, 16PSK, 32PSK, quadrature amplitude modulation (QAM)16, QAM64, QAM256, and QAM1024, and using a transfer learning approach to adapt popular image classification models like MobileNetV2, ResNetV2 (50, 101, and 152 layers), Xception, InceptionV3, and InceptionResNetV2 for SNR estimation. Based on these estimates, the master sensor adjusts the transmission power of the slave sensors to maintain optimal performance while saving energy. Results show that InceptionV3 delivers the best accuracy, making it ideal for our application. InceptionV3 successfully classifies SNRs between 0 and 20 dB for all modulation schemes, achieving an accuracy of over 90%, while lighter models, such as MobileNetV2 and Xception, yielded accuracies as low as 44% for complex, high-order modulation schemes like QAM256 and QAM1024. This result underscores the InceptionV3's reliability in identifying different SNR levels. This approach can facilitate sensors in the network use power more efficiently while maintaining reliable communication.
dc.identifier.doi10.1007/s11276-026-04177-5
dc.identifier.issn1022-0038
dc.identifier.issn1572-8196
dc.identifier.scopus2-s2.0-105042164990
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s11276-026-04177-5
dc.identifier.urihttps://hdl.handle.net/20.500.12684/23662
dc.identifier.wosWOS:001796234800001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofWireless Networks
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260623
dc.subject[Keyword Not Available]
dc.titleDeep learning aided transmission power control for wireless sensor networks
dc.typeArticle

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