In this paper, we investigate secure relay selection for finite-state Markov channel and propose a Q-learning assisted relay selection scheme. Specifically, we firstly analyze the achievable effective secrecy throughput of random selection scheme and optimal selection scheme, respectively, showing that the secrecy performance is highly determined by relay selection methodology. Then, we leverage the Q-learning to learn how to select relay for finite-state Markov channel, which is capable of selecting proper relay with outdated channel state information. Numerical results demonstrate that our proposed Q-learning assisted relay selection scheme can achieve a significant improvement of effective secrecy throughput even with outdated channel information.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Learning-Assisted Secure Relay Selection with Outdated CSI for Finite-State Markov Channel


    Beteiligte:
    Lu, Jianzhong (Autor:in) / He, Dongxuan (Autor:in) / Wang, Zhaocheng (Autor:in)


    Erscheinungsdatum :

    2021-04-01


    Format / Umfang :

    2816534 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Partial Relay Selection With Outdated Channel State Estimation in Mixed RF/FSO Systems

    Petkovic, M. I. / Cvetkovic, A. M. / Djordjevic, G. T. et al. | British Library Online Contents | 2015


    Partial Variable-Gain AF Relay Selection with Outdated Channel Estimates in Spectrum-Sharing Networks

    Moualeu, Jules M. / Hamouda, Walaa / Takawira, Fambirai | IEEE | 2016



    Power Allocation and Performance of Multiuser Mixed RF/FSO Relay Networks With Opportunistic Scheduling and Outdated Channel Information

    Salhab, A. M. / Al-Qahtani, F. S. / Radaydeh, R. M. et al. | British Library Online Contents | 2016


    Deep Learning Empowered Secure RIS-Assisted Non-Terrestrial Relay Networks

    Huang, Chong / Chen, Gaojie / Zhou, Yitong et al. | IEEE | 2022