Robust Design for IRS-Assisted MISO-NOMA Systems: A DRL-Based Approach
Robust Design for IRS-Assisted MISO-NOMA Systems: A DRL-Based Approach
复制标题
DOI：
10.1109/lwc.2023.3335622
复制
发表时间：
2024-03
影响因子：
6.3
通讯作者：
Abdulhamed Waraiet;K. Cumanan;Zhiguo Ding;O. Dobre
中科院分区：
文献类型：
--
作者：
Abdulhamed Waraiet;K. Cumanan;Zhiguo Ding;O. Dobre
在这封信中，我们提出了一种智能反射面（IRS）辅助多输入单输出（MISO）非正交多址（NOMA）系统的鲁棒设计。特别是，通过IRS元件考虑了直接链路和反射链路的信道不确定性，从而制定了遍历和速率最大化问题。基于统计信道状态信息误差模型，对不完全信道估计下的无界信道不确定性进行了数学建模。然而，考虑中断约束的遍历和速率最大化问题在波束形成矢量和IRS单元相移方面不是联合凸的，因此无法用传统的优化算法求解。为了解决非凸性问题并开发联合设计，将具有挑战性的稳健设计重新表述为强化学习（RL）环境。在信道不确定性和服务质量约束条件下，开发了两种深度RL智能体，共同优化IRS单元的波束形成矢量和相移。仿真结果验证了所提出的代理在固定信道和动态信道上的性能。
In this letter, we propose a robust design for an intelligent reflecting surface (IRS)-aided multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system. In particular, the ergodic sum-rate maximization problem is formulated by taking into account the channel uncertainties of both direct links and the reflected links through IRS elements. The unbounded channel uncertainties with imperfect channel estimation are mathematically modelled based on the statistical channel state information (CSI) error model. However, the formulated ergodic sum-rate maximization problem with the outage-constraints is not jointly convex in terms of the beamforming vectors and the phase shifts of IRS elements, and hence it cannot be solved with the conventional optimization algorithms. To address the non-convexity issues and develop a joint design, the challenging robust design is reformulated as a reinforcement learning (RL) environment. Two deep RL agents are developed to jointly optimize the beamforming vectors and phase shifts of the IRS elements with the channel uncertainties and quality of service constraints. Simulation results are provided to validate the performance of the proposed agents for both fixed and dynamic channels.