论文标题
可重新配置的智能表面增强的设备到设备通信
Reconfigurable Intelligent Surface Enhanced Device-to-Device Communications
论文作者
论文摘要
可重新配置的智能表面（RIS）技术是增强设备对设备（D2D）通信的有前途的方法。为了最大程度地提高细胞和D2D网络的总和速率，本文考虑了D2D通信中RIS的联合优化和RIS的相移。为了解决非convex和最大问题，我们提出了一种基于新型的卷积神经网络（CNN）的深Q-NETWORK（DQN），该Q-NETWORK（DQN）共同优化RIS位置及其相移的复杂性较低。数值结果表明，与基准算法相比，所提出的算法可以达到更高的总和利率，同时满足D2D接收器和基站（BS）的服务质量（QOS）要求。
Reconfigurable intelligent surface (RIS) technology is a promising method to enhance the device-to-device (D2D) communications. To maximize the sum rate of the cellular and D2D networks, a joint optimization of the position and the phase shift of RIS in D2D communications is considered in this paper. To solve the non-convex sum rate maximum problem, we propose a novel convolutional neural network (CNN) based deep Q-network (DQN) that jointly optimizes the RIS position and its phase shift with lower complexity. Numerical results illustrate that the proposed algorithm can achieve higher sum rate compared to the benchmark algorithms, meanwhile meeting the quality of service (QoS) requirements at D2D receivers and the base station (BS).
