In this paper, we study the energy efficiency (EE) performance of a three-node underwater full-duplex relay network, where the relay is an energy harvesting node. Since the arrival of harvested energy is intermittent from the ambient environment, energy-efficient data transmission can prolong the lifespan of the network. By exploiting the causal system information, we aim to maximize the long-term end-to-end EE of the network through adaptive power control at the relay node. The system is described through a Markov decision process, and the reinforcement learning framework is applied to obtain the energy-efficient transmission policy. Simulation results demonstrate the long-term average EE performance of the obtained transmission policy, which outperforms two benchmark approaches.


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    Titel :

    Reinforcement Learning-based Energy-Efficient Power Allocation for Underwater Full-Duplex Relay Network with Energy Harvesting


    Beteiligte:
    Wang, Ranning (Autor:in) / Makled, Esraa A. (Autor:in) / Yadav, Animesh (Autor:in) / Dobre, Octavia A. (Autor:in) / Zhao, Ruiqin (Autor:in)


    Erscheinungsdatum :

    01.11.2020


    Format / Umfang :

    979986 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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