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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    Title :

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


    Contributors:


    Publication date :

    2020-11-01


    Size :

    979986 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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