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- W2907002301 abstract "Device-to-Device (D2D) communication has gained interest as a promising technology for next generation wireless networks. D2D communication promotes the use of point-to-point communications between users without going through the base stations. In this paper, we aim at maximizing the sum rate of a D2D network, under the assumption of realistic time-varying channels and D2D interference. Specifically, we formulate channels as Finite-State Markov Channels (FSMC). With realistic FSMC, the complexity of the problem is high. Consequently, we propose the use of a centralized Deep Reinforcement Learning (DRL) transmission scheme for D2D communications, where transmission decisions are taken by one agent that has a global knowledge of the D2D network. We compare the DRL-based scheme with other transmission schemes. The results show that it outperforms other approaches in terms of achieved sum rate." @default.
- W2907002301 created "2019-01-11" @default.
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- W2907002301 date "2018-10-01" @default.
- W2907002301 modified "2023-10-18" @default.
- W2907002301 title "Deep Reinforcement Learning-based Data Transmission for D2D Communications" @default.
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- W2907002301 doi "https://doi.org/10.1109/wimob.2018.8589114" @default.
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