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- W4313303737 abstract "In this work, a two-stage deep reinforcement learning (DRL) approach is presented for a full-duplex (FD) transmission scenario that does not depend on the channel state information (CSI) knowledge to predict the phase-shifts of reconfigurable intelligent surface (RIS), beamformers at the base station (BS), and the transmit powers of BS and uplink users in order to maximize the weighted sum rate of uplink and downlink users. As the self-interference (SI) cancellation and beamformer design are coupled problems, the first stage uses a least squares method to partially cancel self-interference (SI) and initiate learning, while the second stage uses DRL to make predictions and achieve performance close to methods with perfect CSI knowledge. Further, to reduce the signaling from BS to the RISs, a DRL framework is proposed that predicts quantized RIS phase-shifts and beamformers using $32$ times fewer bits than the continuous version. The quantized methods have reduced action space and therefore faster convergence; with sufficient training, the UL and DL rates for the quantized phase method are $8.14%$ and $2.45%$ better than the continuous phase method respectively. The RIS elements can be grouped to have similar phase-shifts to further reduce signaling, at the cost of reduced performance." @default.
- W4313303737 created "2023-01-06" @default.
- W4313303737 creator A5008700987 @default.
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- W4313303737 date "2022-12-28" @default.
- W4313303737 modified "2023-09-23" @default.
- W4313303737 title "A DRL Approach for RIS-Assisted Full-Duplex UL and DL Transmission: Beamforming, Phase Shift and Power Optimization" @default.
- W4313303737 doi "https://doi.org/10.48550/arxiv.2212.13854" @default.
- W4313303737 hasPublicationYear "2022" @default.
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