Delay-Doppler Frequency Domain-Aided Superimposing Pilot OTFS Channel Estimation Based on Deep Learning

被引：0
|
作者：
Yang, Chaoyi [1 ]
Wang, Junlong [1 ]
Pan, Zhenni [1 ]
Shimamoto, Shigeru [1 ]
机构：
[1] Waseda Univ, Dept Commun & Comp Engn, Tokyo, Japan
来源：
2022 IEEE 96TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-FALL) | 2022年
关键词：
OTFS; superimposing pilot; machine learning; ResNet; channel estimation;
D O I：
10.1109/VTC2022-Fall57202.2022.10012759
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this work, a channel estimation method for OTFS using superimposing pilot is proposed. The pilot is superimposing on the first transmitter data symbol, yielding an enhanced frequency domain of Delay-Doppler domain pattern at the receiver end. A deep convolution neural network is proposed to de-noise the interfered channel matrix. Simulation results show that the bit error rate performance of the proposed method is better than that of the existing methods at low pilot energy.
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收藏
页数：6
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