An active reconfigurable intelligent surface (RIS)-aided multi-user downlink
communication system is investigated, where non-orthogonal multiple access
(NOMA) is employed to improve spectral efficiency, and the active RIS is
powered by energy harvesting (EH). The problem of joint control of the RIS's
amplification matrix and phase shift matrix is formulated to maximize the
communication success ratio with considering the quality of service (QoS)
requirements of users, dynamic communication state, and dynamic available
energy of RIS. To tackle this non-convex problem, a cascaded deep learning
algorithm namely long short-term memory-deep deterministic policy gradient
(LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed
to predict users' dynamic communication state. Then, based on the prediction
results, a DDPG based algorithm is proposed to joint control the amplification
matrix and phase shift matrix of the RIS. Finally, simulation results verify
the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate
that the LSTM-DDPG algorithm has a significant advantage over other benchmark
algorithms in terms of communication success ratio performance.

该论文提出一种基于能量收集的可重构智能表面帮助非正交多用户下行通信系统提高谱效率的新方法，使用深度学习算法解决由于用户通信状态的动态变化和可利用能源的动态变化影响的非凸问题，获得了比其他基准算法更好的通信成功率表现。