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- W2935921761 abstract "A multi-antenna wireless transmitter communicates with its information receiver while beaming the radiated power to multiple nearby radio-frequency energy harvesters. The transmitter knows the channel to the information receiver but not the ones to the energy harvesters. By designing its transmit covariance matrix, the transmitter maximizes the minimum harvested power among the multiple energy harvesters while maintaining the information rate toward the receiver. To achieve this, we introduce a simplified channel vector from the transmitter toward the energy harvester. It can be estimated through particular transmissions and very limited feedback from the energy harvester to the transmitter. Once the transmitter obtains the simplified channel vectors, it can find the optimal transmit power allocation. To avoid high computational complexity, we propose a method to find the optimal power allocation with a deep neural network instead of solving a convex optimization problem. The simplified channel vectors are the input to the deep neural network. The neural network is trained offline with a large number of simulated data. Simulation results validate the method and show its superior performance compared with the convex optimization approach." @default.
- W2935921761 created "2019-04-25" @default.
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- W2935921761 date "2018-08-01" @default.
- W2935921761 modified "2023-10-14" @default.
- W2935921761 title "Deep Learning for Optimized Wireless Transmission to Multiple RF Energy Harvesters" @default.
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- W2935921761 doi "https://doi.org/10.1109/vtcfall.2018.8690775" @default.
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