论文标题
Turbo-ai：基于迭代机器学习的频道估计2D大型阵列
Turbo-AI: Iterative Machine Learning Based Channel Estimation for 2D Massive Arrays
论文作者
论文摘要
最近，机器学习（ML）被公认为是无线通信的有效工具，并扮演着进化角色，以增强第五代（5G）和5G（B5G）系统的物理层（PHY）。在本文中，我们关注2-维（2D）大型天线阵列的基于ML的通道估计。由于对2D阵列进行了普通培训的高度计算要求，我们利用2D Kronecker协方差模型独立地对垂直和水平空间领域进行子空间培训，这可以为M（M^4n^4）/O（Mn^4 + Nm + Nm^4）$ ML与ML $ M \ m \ 2D $ 2D ray达成复杂性的成本储蓄因子$ O（m^4n^4）/O（m^4n^4）/o（mn^4n^4）/o（此外，我们提出了一种迭代培训方法，称为涡轮增压。除了子空间训练外，新方法可以单调地减少观察添加噪声的有效差异，并通过重新训练更新神经网络（NN）模型。此外，我们提出了一个名为“通用培训”的概念。它允许将一个NN用于广泛的信噪比（SNR）操作点和空间角度，这可以极大地简化涡轮增压。数值结果表明，涡轮增压ai可以紧密接近与精灵辅助通道估计的结合，尤其是在低SNR处。
Recently, Machine Learning (ML) is recognized as an effective tool for wireless communications and plays an evolutionary role to enhance Physical Layer (PHY) of 5th Generation (5G) and Beyond 5G (B5G) systems. In this paper, we focus on the ML-based channel estimation for 2- Dimensional (2D) massive antenna arrays. Due to the extremely high computational requirement for 2D arrays with Ordinary Training, we exploit 2D Kronecker covariance model to perform Subspace Training for vertical and horizontal spatial domain independently, which achieves a complexity cost saving factor $O(M^4N^4)/O(MN^4 + NM^4)$ for ML with an $M \times N$ 2D array. Furthermore, we propose an iterative training approach, referred to as Turbo-AI. Along with Subspace Training, the new approach can monotonically reduce the effective variance of additive noise of the observation, and update the Neural Network (NN) models by re-training. Furthermore, we propose a concept, named Universal Training. It allows to use one NN for a wide range of Signal-to-Noise-Ratio (SNR) operation points and spatial angles, which can greatly simplify Turbo-AI usage. Numerical results exhibit that Turbo-AI can tightly approach the genie-aided channel estimation bound, especially at low SNR.
