TY - GEN
T1 - ChanEstNet
T2 - 2019 IEEE International Conference on Communications, ICC 2019
AU - Liao, Yong
AU - Hua, Yuanxiao
AU - Dai, Xuewu
AU - Yao, Haimei
AU - Yang, Xinyi
N1 - Funding information: This work was supported by the National Natural Science Foundation of China (No. 61501066), the Chongqing Frontier and Applied Basic Research Project (No. cstc2015jcyjA40003), the graduate research and innovation
foundation of Chongqing, China (No. CYS18061), and the Fundamental Research Funds for the Central Universities (No. 106112017CDJXY500001).
PY - 2019/5
Y1 - 2019/5
N2 - Aiming at the problem that the downlink channel estimation performance is limited due to the fast time-varying and non-stationary characteristics in the high-speed mobile scenarios, we propose a channel estimation network based on deep learning, called ChanEstNet. ChanEstNet uses the convolutional neural network (CNN) to extract channel response feature vectors and recurrent neural network (RNN) for channel estimation. We use a large amount of high-speed channel data to conduct offline training for the learning network, fully exploit the channel information in the training sample, make it learn the characteristics of fast time-varying and non-stationary channels, and better track the features of channels changing in high-speed environments. The simulation results show that in the high-speed mobile scenarios, compared with the traditional methods, the proposed channel estimation method has low computational complexity and significant performance improvement.
AB - Aiming at the problem that the downlink channel estimation performance is limited due to the fast time-varying and non-stationary characteristics in the high-speed mobile scenarios, we propose a channel estimation network based on deep learning, called ChanEstNet. ChanEstNet uses the convolutional neural network (CNN) to extract channel response feature vectors and recurrent neural network (RNN) for channel estimation. We use a large amount of high-speed channel data to conduct offline training for the learning network, fully exploit the channel information in the training sample, make it learn the characteristics of fast time-varying and non-stationary channels, and better track the features of channels changing in high-speed environments. The simulation results show that in the high-speed mobile scenarios, compared with the traditional methods, the proposed channel estimation method has low computational complexity and significant performance improvement.
UR - https://www.scopus.com/pages/publications/85070187897
U2 - 10.1109/ICC.2019.8761312
DO - 10.1109/ICC.2019.8761312
M3 - Conference contribution
AN - SCOPUS:85070187897
SN - 9781538680896
T3 - IEEE International Conference on Communications
SP - 1272
EP - 1277
BT - ICC 2019 - 2019 IEEE International Conference on Communications (ICC 2019) - Proceedings
PB - IEEE
CY - Piscataway, NJ
Y2 - 20 May 2019 through 24 May 2019
ER -