A radio-frequency (RF) energy harvester collects the radiated energy from nearby wireless information transmitters. Multiple wireless transmitters concentrate their radiation on the RF energy harvester while satisfying the basic requirement of the information links. To achieve this, a deep learning method is proposed for the multiuser transmission. A deep neural network (DNN) is implemented in each wireless transmitter. The DNNs are trained offline with simulated channels and applied online to generate transmit covariance matrices that meet the communication requirement and approach the maximum sum received power at the RF energy harvester.


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    Title :

    Deep Learning for Radio-Frequency Energy Harvesting with Multiple Wireless Transmitters


    Contributors:
    Qian, Yuchen (author) / Xing, Yuan (author) / Dong, Liang (author)


    Publication date :

    2018-08-01


    Size :

    412395 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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