论文标题
一种深度学习和基于地理空间的基于数据的渠道估计技术，用于混合大型MIMO系统
A Deep Learning and Geospatial Data-Based Channel Estimation Technique for Hybrid Massive MIMO Systems
论文作者
论文摘要
本文使用基于角的混合编码（AB-HP）提供了多用户大量多输入多输出（MU-MMIMO）系统的新通道估计技术。提出的通道估计技术通过深神经网络（DNN）和模糊C-Means（FCM）群集生成了服务区域中用户终端（UT）区域的小组通道状态信息（CSI）。服务区域中基站（BS）和可行的UT位置之间的慢时间变化的CSI是通过离线射线跟踪和与一维卷积神经网络（1D-CNN）和回归树的基于1维卷积神经网络（1D-CNN）相关的基于DNN的路径估计模型来计算的。然后，通过建议的FCM聚类将所有可行位置的UT级CSI分组为簇。最后，将服务区域分为许多非重叠的UT区域。每个UT区域的特征是一组名为UT组CSI的簇集，该集群在AB-HP的模拟RF射流器设计中用于减少MU-MMIMO系统中所需的大型在线CSI开销。然后，在AB-HP的基带（BB）预码器中使用了缩小的在线CSI。模拟在28 GHz的室内场景中进行，并在AB-HP MU-MIMOMO系统中进行了测试，其矩形阵列（URA）具有16x16 = 256天线和22个RF链。说明性结果表明，与传统的在线频道相比，使用建议的离线频道估计技术可以减少91.4％的在线CSI。与计算昂贵的射线跟踪相比，拟议的基于DNN的路径估计技术可产生相同数量的UT级CSI，运行时降低了65.8％。
This paper presents a novel channel estimation technique for the multi-user massive multiple-input multiple-output (MU-mMIMO) systems using angular-based hybrid precoding (AB-HP). The proposed channel estimation technique generates group-wise channel state information (CSI) of user terminal (UT) zones in the service area by deep neural networks (DNN) and fuzzy c-Means (FCM) clustering. The slow time-varying CSI between the base station (BS) and feasible UT locations in the service area is calculated from the geospatial data by offline ray tracing and a DNN-based path estimation model associated with the 1-dimensional convolutional neural network (1D-CNN) and regression tree ensembles. Then, the UT-level CSI of all feasible locations is grouped into clusters by a proposed FCM clustering. Finally, the service area is divided into a number of non-overlapping UT zones. Each UT zone is characterized by a corresponding set of clusters named as UT-group CSI, which is utilized in the analog RF beamformer design of AB-HP to reduce the required large online CSI overhead in the MU-mMIMO systems. Then, the reduced-size online CSI is employed in the baseband (BB) precoder of AB-HP. Simulations are conducted in the indoor scenario at 28 GHz and tested in an AB-HP MU-mMIMO system with a uniform rectangular array (URA) having 16x16=256 antennas and 22 RF chains. Illustrative results indicate that 91.4% online CSI can be reduced by using the proposed offline channel estimation technique as compared to the conventional online channel sounding. The proposed DNN-based path estimation technique produces same amount of UT-level CSI with runtime reduced by 65.8% as compared to the computationally expensive ray tracing.
