Deep Learning Based Antenna-time Domain Channel Extrapolation for Hybrid mmWave Massive MIMO
Deep Learning Based Antenna-time Domain Channel Extrapolation for Hybrid mmWave Massive MIMO
复制标题
基于深度学习的混合毫米波大规模 MIMO 天线时域信道外推
DOI：
10.1109/tvt.2022.3197452
复制
发表时间：
2021
影响因子：
6.8
通讯作者：
Octavia A. Dobre
中科院分区：
文献类型：
--
作者：
Shunbo Zhang;Shun Zhang;Jianpeng Ma;Tian Liu;Octavia A. Dobre
在时变大规模多输入多输出（MIMO）系统中，由于下行链路训练和上行链路反馈的开销过高，在基站获取下行链路信道状态信息是一项非常具有挑战性的任务。在本文中，我们考虑在BS的混合预编码结构，并检查天线时域信道外推。我们设计了一个潜在的常微分方程（ODE）为基础的网络变分自动编码器（VAE）的框架下学习的映射函数，从部分上行链路的信道到完整的下行链路的BS侧。具体地说，编码器采用门控递归单元，解码器采用全连接神经网络。利用端到端的学习来优化网络参数。仿真结果表明，所设计的网络可以有效地从部分上行信道中推断出完整的下行信道，从而显着减少信道训练开销。
In a time-varying massive multiple-input multipleoutput (MIMO) system, the acquisition of the downlink channel state information at the base station (BS) is a very challenging task due to the prohibitively high overheads associated with downlink training and uplink feedback. In this paper, we consider the hybrid precoding structure at BS and examine the antennatime domain channel extrapolation. We design a latent ordinary differential equation (ODE)-based network under the variational auto-encoder (VAE) framework to learn the mapping function from the partial uplink channels to the full downlink ones at the BS side. Specifically, the gated recurrent unit is adopted for the encoder and the fully-connected neural network is used for the decoder. The end-to-end learning is utilized to optimize the network parameters. Simulation results show that the designed network can efficiently infer the full downlink channels from the partial uplink ones, which can significantly reduce the channel training overhead.