大规模MIMO系统中基于dnn的CSI-RS端口虚拟化矩阵设计

Dongheon Lee, Seongyeop Joung, Sooyong Choi
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引用次数: 1

摘要

在大规模多输入多输出(MIMO)系统中,信道状态信息(CSI)是实现高数据速率的必要条件。但是,由于信道状态信息参考信号(CSI- rs)的开销较大,很难获得全天线的CSI。因此,采用了CSI-RS端口虚拟化矩阵,该矩阵将基站的单个CSI-RS端口中的天线分组。针对时分双工(TDD)大规模MIMO系统,提出了一种基于深度神经网络(DNN)的CSI-RS端口虚拟化矩阵设计方案。该方案同时利用CSI- rs和探测参考信号来获取CSI,并根据码本索引估计CSI- rs端口虚拟化矩阵。仿真结果表明,提出的基于深度神经网络的方案可以获得25.8%的性能增益,特别是在高上行信噪比区域,可以获得更准确的上行信道信息。
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DNN-based CSI-RS Port Virtualization Matrix Design in Massive MIMO System
In massive multiple-input multiple-output (MIMO) systems, the channel state information (CSI) is needed to achieve high data rate. However, getting the CSI of the full antennas is hard since channel state information reference signal (CSI-RS) overhead is large. Therefore the CSI-RS port virtualization matrix which groups the antennas in a single CSI-RS port at the base station is used. In this paper, we propose a deep neural network (DNN) based CSI-RS port virtualization matrix design scheme for a time division duplex (TDD) massive MIMO system. The proposed scheme utilizes both the CSI-RS and sounding reference signal to get the CSI and estimates the CSI-RS port virtualization matrix in terms of codebook index. Simulation result shows that the proposed DNN based scheme can achieve up to 25.8% performance gain, particularly in high uplink signal-to-noise ratio regions where more accurate uplink channel information can be obtained.
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