无需信道估计，用接收导频信号直接优化毫米波MIMO的RIS相位，提升通信速率。
Capacity-Net-Based RIS Precoding Design without Channel Estimation for mmWave MIMO System
- 利用接收导频信号隐式建模信道，跳过传统信道估计步骤。
- 通过无监督学习直接映射导频信号与最优相位配置，实现速率最大化。
- 特别适合缺乏精确信道信息的毫米波系统，部署更灵活高效。
本文提出Capacity-Net，一种新型无监督学习方法，旨在最大化反射智能表面（RIS）辅助毫米波（mmWave）多输入多输出（MIMO）系统的可达速率。为应对毫米波频段严重的信道衰落，需优化RIS中反射元件的相位偏移以提升系统性能。然而，多数优化算法严重依赖完整且准确的信道状态信息（CSI），而由于RIS主要由无源组件构成，获取该信息极具挑战性。为克服此难题，我们采用无监督学习技术，利用接收导频信号隐式提供信道信息。通常，评估可达速率需要精确的完美CSI，而本方法不进行显式信道估计，而是建立接收导频信号、优化后的RIS相位偏移与最终可达速率之间的映射关系。仿真结果表明，所提基于Capacity-Net的无监督学习方法在性能上优于依赖传统信道估计的学习方法。
原文摘要 · Abstract (English)
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.
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