A novel reconfigurable intelligent surface (RIS) aided non-orthogonal
multiple access (NOMA) downlink transmission framework is proposed. We
formulate a long-term stochastic optimization problem that involves a joint
optimization of NOMA user partitioning and RIS phase shifting, aiming at
maximizing the sum data rate of the mobile users (MUs) in NOMA downlink
networks. To solve the challenging joint optimization problem, we invoke a
modified object migration automation (MOMA) algorithm to partition the users
into equal-size clusters. To optimize the RIS phase-shifting matrix, we propose
a deep deterministic policy gradient (DDPG) algorithm to collaboratively
control multiple reflecting elements (REs) of the RIS. Different from
conventional training-then-testing processing, we consider a long-term
self-adjusting learning model where the intelligent agent is capable of
learning the optimal action for every given state through exploration and
exploitation. Extensive numerical results demonstrate that: 1) The proposed
RIS-aided NOMA downlink framework achieves an enhanced sum data rate compared
with the conventional orthogonal multiple access (OMA) framework. 2) The
proposed DDPG algorithm is capable of learning a dynamic resource allocation
policy in a long-term manner. 3) The performance of the proposed RIS-aided NOMA
framework can be improved by increasing the granularity of the RIS phase
shifts. The numerical results also show that reducing the granularity of the
RIS phase shifts and increasing the number of REs are two efficient methods to
improve the sum data rate of the MUs.

提出了一种新颖的可重构智能表面辅助的非正交多址 (NOMA) 下行传输框架。该框架采用了长期随机优化问题和改进的移动对象迁移自动化算法 (MOMA) 来优化用户分组和可重构智能表面相位移动矩阵，结合深度确定性策略梯度 (DDPG) 算法来控制多重反射元素，实现了动态资源分配策略。通过多个数值结果的分析表明，该方法相对于传统正交多址 (OMA) 框架提高了移动用户 (MUs) 的总数据率，可以学会动态资源分配策略，同时通过提高可重构智能表面相位移动的粒度和增加反射元素的数量来提高数据收益。