A backscatter-assisted relaying network (BRN) has been recently proposed to improve data rate, transmission range, and energy efficiency of D2D communications. In the BRN, as a D2D transmitter actively transmits data to a receiver in its time slot, other D2D transmitters can act as relays, i.e., helpers, through backscattering signal from the D2D transmitter to the receiver. This passive relay method has shown to be effective in terms of diversity gain. However, this impairs energy harvesting by the helpers and thus degrades their active data transmission performance. Therefore, the problem in the BRN is to optimize backscatter relaying policies, i.e., reflection coefficients, for the helpers to maximize the total network throughput over time slots. Finding the optimal decisions is generally challenging as energy in batteries, i.e., energy states, of the helpers and communication channels are dynamic and uncertain. In this letter, we propose to adopt the Deep Deterministic Policy Gradient (DDPG) algorithm to determine the optimal reflection coefficients of the helpers. The simulation results show that the proposed DRL scheme significantly improves the throughput performance.

中文翻译：

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反向散射辅助中继通信的深度强化学习方法

最近提出了一种反向散射辅助中继网络 (BRN)，以提高 D2D 通信的数据速率、传输范围和能源效率。在BRN中，由于D2D发送器在其时隙内主动向接收器发送数据，其他D2D发送器可以充当中继器，即助手，通过从D2D发送器到接收器的反向散射信号。这种无源中继方法在分集增益方面已被证明是有效的。然而，这会损害助手的能量收集，从而降低其主动数据传输性能。因此，BRN 中的问题是优化反向散射中继策略，即反射系数，使助手在时隙上最大化网络总吞吐量。寻找最佳决策通常具有挑战性，因为电池中的能量，即能量状态，帮助者和沟通渠道的数量是动态的和不确定的。在这封信中，我们建议采用深度确定性策略梯度（DDPG）算法来确定助手的最佳反射系数。仿真结果表明，所提出的DRL方案显着提高了吞吐量性能。