Security and energy efficiency have become crucial features in the modern-era wireless communication. In this paper, we consider an energy-efficient design for intelligent reflecting surface (IRS)-assisted multiple-input multiple-output multiple-eavesdropper (MIMOME) wiretap channels (WTC). Our objective is to jointly optimize the transmit covariance matrix and the IRS phase-shifts to maximize the secrecy energy efficiency (SEE) of the considered system subject to a secrecy rate constraint at the legitimate receiver. To tackle this challenging non-convex problem in which the design variables are coupled in the objective and the constraint, we propose a penalty dual decomposition based alternating gradient projection (PDDAPG) method to obtain an efficient solution. We also show that the computational complexity of the proposed algorithm grows only linearly with the number of reflecting elements at the IRS, as well as with the number of antennas at transmitter/receivers’ nodes. Our results confirm that using an IRS is helpful to improve the SEE of MIMOME WTC compared to its no-IRS counterpart only when the power consumption at IRS is small. In particular, and a large-sized IRS is not always beneficial for the SEE of a MIMOME WTC.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    On the Energy-Efficiency Maximization for IRS-Assisted MIMOME Wiretap Channels


    Beteiligte:


    Erscheinungsdatum :

    2022-09-01


    Format / Umfang :

    611608 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    On the Average Secrecy Capacity for FSO Wiretap Channels with Nonzero Boresight Pointing Errors

    Boluda-Ruiz, Ruben / Castillo-Vazquez, Carmen / Castillo-Vazquez, Beatriz et al. | IEEE | 2018



    Energy Efficiency Maximization of AF Relaying SWIPT Systems with Energy Recycling

    Li, Chuanping / Wu, Peiran / Xia, Minghua | IEEE | 2019