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Deep Learning for Outage Probability Minimization in Secure NOMA Energy Harvesting UAV IoT Networks

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Abstract

In this paper, a secure uplink non-orthogonal multiple access (NOMA) internet of things (IoT) system using an energy harvesting (EH) unmanned aerial vehicle (UAV) is studied. The communication protocol includes three phases: The first phase is an EH phase, in which a UAV relay (UR) and IoT user/devices (IDs) harvest radio frequency energy from a base station (BS). Information transmission from IDs to the UAV relay is the second phase. The third phase then relays the information from the UR to the BS. Furthermore, in the second and third phases, a UAV eavesdropper (UE) wiretaps the signals from the IDs and UAV relay. For this system, we derived the closed-form outage and intercept probabilities to evaluate the system and secrecy performances. We then propose a random-cut continuous genetic algorithm (RCGA) to minimize the outage probability and obtain training data. Finally, we train a deep learning model to predict the optimal configuration parameters, allowing for rapid adaptation to environmental conditions.

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Data Availability

No datasets were generated or analysed during the current study.

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Vietnam

    Nguyen Quoc Long, Viet-Hung Dang, Nhu Gia Nguyen, Thanh Trong Nguyen & Van Nhan Vo

  2. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Nguyen Quoc Long, Viet-Hung Dang, Nhu Gia Nguyen, Thanh Trong Nguyen & Van Nhan Vo

  3. Department of Information Security and Commuication Technology, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology - NTNU, O.S. Bragstads plass 2a, 7491, Trondheim, Norway

    Tu Dac Ho

  4. Faculty of Computer Science, Phenikaa University, Yen Nghia Ward, Hanoi, Ha Dong District, 100000, Vietnam

    Hung Tran

  5. Interdisciplinary Center for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg City, 1855, Luxembourg

    Duc-Dung Tran

  6. The University of Danang, Vietnam - Korea University of Information and Communication Technology, Da Nang, 50000, Vietnam

    Cong Le Thanh

Authors
  1. Nguyen Quoc Long
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Contributions

Nguyen Quoc Long, Viet-Hung Dang and Van Nhan Vo wrote the main manuscript text. Nguyen Trong Thanh, Tu Dac Ho wrote the numerical results. Hung Tran, Duc-Dung Tran, Cong Le Thanh edited the algorithms. All authors reviewed the manuscript.

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Correspondence to Viet-Hung Dang.

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Long, N.Q., Dang, VH., Nguyen, N.G. et al. Deep Learning for Outage Probability Minimization in Secure NOMA Energy Harvesting UAV IoT Networks. Mobile Netw Appl 28, 2275–2287 (2023). https://doi.org/10.1007/s11036-024-02348-2

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