Uncertainty Aware Deep Learning Model for Secure and Trustworthy Channel Estimation in 5G Networks

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Catak F. O., Cali U., Kuzlu M., Sarp S.

12th Mediterranean Conference on Embedded Computing, MECO 2023, Budva, Karadağ, 6 - 10 Haziran 2023 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/meco58584.2023.10155011
  • Basıldığı Şehir: Budva
  • Basıldığı Ülke: Karadağ
  • Anahtar Kelimeler: Channel estimation, Trustworthy AI, Uncertainty, Wireless network security
  • Yozgat Bozok Üniversitesi Adresli: Hayır

Özet

With the rise of intelligent applications, such as self-driving cars and augmented reality, the security and reliability of wireless communication systems have become increasingly crucial. One of the most critical components of ensuring a high-quality experience is channel estimation, which is fundamental for efficient transmission and interference management in wire-less networks. However, using deep neural networks (DNNs) in channel estimation raises security and trust concerns due to their complexity and the need for more transparency in decision-making. This paper proposes a Monte Carlo Dropout (MCDO)-based approach for secure and trustworthy channel estimation in 5G networks. Our approach combines the advantages of traditional and deep learning techniques by incorporating conventional pilot-based channel estimation as a prior in the deep learning model. Additionally, we use MCDO to obtain uncertainty-aware predictions, enhancing the model's security and trustworthiness. Our experiments demonstrate that our proposed approach outper-forms traditional and deep learning-based approaches regarding security, trustworthiness, and performance in SG scenarios.