论文标题
2D和3D卷积神经网络对MIMO频道估计的研究
A Study on MIMO Channel Estimation by 2D and 3D Convolutional Neural Networks
论文作者
论文摘要
在本文中，我们研究了卷积神经网络（CNN）估计量的用法多输入 - 型 - 型 - 次数 - 输出正交频施加多路复用（MIMO-OFDM）通道估计（CE）。具体而言，CNN估计器插值用于估算完整OFDM资源元素（RE）矩阵的通道的参考信号的通道值。我们设计了一个基于U-NET的2D CNN体系结构，以及用于处理空间相关性的3D CNN体系结构。我们研究了根据5G NR标准生成的各种CNN体系结构的性能，尤其是我们研究了空间相关，多普勒和参考信号资源分配的影响。然后将CE CNN估计器与MIMO检测算法集成，以测试其对系统级别位错误率（BER）性能的影响。
In this paper, we study the usage of Convolutional Neural Network (CNN) estimators for the task of Multiple-Input-Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) Channel Estimation (CE). Specifically, the CNN estimators interpolate the channel values of reference signals for estimating the channel of the full OFDM resource element (RE) matrix. We have designed a 2D CNN architecture based on U-net, and a 3D CNN architecture for handling spatial correlation. We investigate the performance of various CNN architectures fora diverse data set generated according to the 5G NR standard and in particular, we investigate the influence of spatial correlation, Doppler, and reference signal resource allocation. The CE CNN estimators are then integrated with MIMO detection algorithms for testing their influence on the system level Bit Error Rate(BER) performance.
