.png){kind=icon}
返回
Deep Learning Based Channel Estimation Algorithm for Fast Time-Varying MIMO-OFDM Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2019.2960242.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Channel estimation is very challenging for multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in high mobility environments with non-stationarity channel characteristics. In order to handle this problem, we propose a deep learning (DL)-based MIMO-OFDM channel estimation algorithm. By performing offline training to the learning network, the channel state information (CSI) generated by the training samples can be effectively utilized to adapt the characteristics of fast time-varying channels in the high mobility scenarios. The simulation results show that the proposed DL-based algorithm is more robust for the scenarios of high mobility in MIMO-OFDM systems, compared to the conventional algorithms.
Keyword:
MIMO-OFDM
channel estimation
fast time-varying channel
deep learning
non-stationarity channel
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
引用论文
Deep Learning-Based Joint Pilot Design and Channel Estimation for Multiuser MIMO Channels基于深度学习的多用户MIMO信道联合导频设计与信道估计
OAAI
OAAI分享收藏A Non-Stationary IMT-Advanced MIMO Channel Model for High-Mobility Wireless Communication SystemsOAAI
OAAI分享收藏Synthesis of soluble copolymers bearing ionic graft for alkaline anion exchange membrane.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏
查看更多内容