Deep Learning-Based Channel Estimation for Massive MIMO Systems
Deep Learning-Based Channel Estimation for Massive MIMO Systems
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DOI：
10.1109/lwc.2019.2912378
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发表时间：
2019-08-01
影响因子：
6.3
通讯作者：
Kim, Il-Min
中科院分区：
文献类型：
--
作者：
Chun, Chang-Jae;Kang, Jae-Mo;Kim, Il-Min
在这封信中，我们提出了一种基于深度学习（DL）的大规模多输入多输出（MIMO）系统信道估计方案。不同于现有的研究，我们开发的导频长度小于发射天线的数量的情况下，信道估计方案。该方案采用两阶段估计过程：1）基于DL的导频辅助信道估计和2）基于DL的数据辅助信道估计。在第一阶段中，通过使用两层神经网络（TNN）和深度神经网络（DNN）来联合设计导频本身和信道估计器。在第二阶段中，通过以迭代方式使用另一DNN来进一步增强信道估计的准确性。仿真结果表明，该信道估计方案比传统的信道估计方案具有更好的性能。我们还得到了一个有用的洞察到最佳导频长度给定的发射天线的数量。
In this letter, we propose a deep learning (DL)-based channel estimation scheme for the massive multiple-input multiple-output (MIMO) system. Unlike existing studies, we develop the channel estimation scheme for the case that the pilot length is smaller than the number of transmit antennas. The proposed scheme takes a two-stage estimation process: 1) a DL-based pilot-aided channel estimation and 2) a DL-based data-aided channel estimation. In the first stage, the pilot itself and the channel estimator are jointly designed by using both a two-layer neural network (TNN) and a deep neural network (DNN). In the second stage, the accuracy of channel estimation is further enhanced by using another DNN in an iterative manner. The simulation results demonstrate that the proposed channel estimation scheme has much better performance than the conventional channel estimation scheme. We also derive a useful insight into the optimal pilot length given the number of transmit antennas.