We consider an Intelligent Reflecting Surface (IRS)-aided multiple-input single-output (MISO) system for downlink transmission. We compare the performance of Deep Reinforcement Learning (DRL) and conventional optimization methods in finding optimal phase shifts of the IRS elements to maximize the user signal-to-noise (SNR) ratio. Furthermore, we evaluate the robustness of these methods to channel impairments and changes in the system. We demonstrate numerically that DRL solutions show more robustness to noisy channels and user mobility.

翻译：我们考虑的是智能反射表面(IRS)辅助的多投入单输出传输系统(MISO),我们比较了深度强化学习(DRL)和常规优化方法的性能,以寻找IRS元素的最佳阶段转换,以最大限度地实现用户信号对噪音的比例。此外,我们评估这些方法的稳健性,以引导系统缺陷和变化。我们从数字上表明,DRL解决方案对噪音频道和用户流动性表现出更强的力度。