In this paper, we propose Capacity-Net, a novel unsupervised learning approach aimed at maximizing the achievable rate in reflecting intelligent surface (RIS)-aided millimeter-wave (mmWave) multiple input multiple output (MIMO) systems. To combat severe channel fading of the mmWave spectrum, we optimize the phase-shifting factors of the reflective elements in the RIS to enhance the achievable rate. However, most optimization algorithms rely heavily on complete and accurate channel state information (CSI), which is often challenging to acquire since the RIS is mostly composed of passive components. To circumvent this challenge, we leverage unsupervised learning techniques with implicit CSI provided by the received pilot signals. Specifically, it usually requires perfect CSI to evaluate the achievable rate as a performance metric of the current optimization result of the unsupervised learning method. Instead of channel estimation, the Capacity-Net is proposed to establish a mapping among the received pilot signals, optimized RIS phase shifts, and the resultant achievable rates. Simulation results demonstrate the superiority of the proposed Capacity-Net-based unsupervised learning approach over learning methods based on traditional channel estimation.

翻译：本文提出了一种名为Capacity-Net的新型无监督学习方法，旨在最大化智能反射面辅助的毫米波多输入多输出系统的可达速率。为应对毫米波频谱的严重信道衰落，我们通过优化RIS中反射单元的相移因子来提升可达速率。然而，大多数优化算法严重依赖于完整且准确的信道状态信息，而由于RIS主要由无源元件构成，获取CSI通常具有挑战性。为规避这一难题，我们利用无监督学习技术，以接收到的导频信号作为隐式CSI。具体而言，评估无监督学习方法当前优化结果的性能指标（即可达速率）通常需要完美CSI。本文提出的Capacity-Net无需进行信道估计，而是直接在接收导频信号、优化的RIS相移与最终可达速率之间建立映射关系。仿真结果表明，所提出的基于Capacity-Net的无监督学习方法优于基于传统信道估计的学习方法。