Residual Learning based Channel Estimation for OTFS system

Qingyu Li1; Yi Gong1; Fanke Meng2; Zhongjie Li3; Linlong Miao1; Zhan Xu1

10.1109/ICCCWorkshops55477.2022.9896637

2022

2474-9133

978-1-6654-5978-5

2022 IEEE/CIC International Conference on Communications in China (ICCC Workshops)

275-280

11-13 Aug. 2022

Sanshui, Foshan, China

Orthogonal time frequency space (OTFS) systems can effectively balance the Doppler shift by transforming the channel with a drastic change in the time-frequency (TF) domain into a stable channel in the delay-Doppler (DD) domain. In order to take full advantage of the OTFS system, accurate channel estimation results are critical in OTFS systems. In this paper, a model-driven deep learning (DL)-based channel estimation technique is proposed for OTFS in the DD domain. The presented channel estimation scheme has two parts. The first part takes advantage of the traditional orthogonal matching pursuit (OMP) algorithm to generate preliminary channel estimation results. The second part uses a deep residual learning network (ResNet) to further process the rough estimation results to get an accurate OTFS channel estimation. Simulation results demonstrate that the performance of the proposed model-driven ResNet-based scheme is significantly better than the traditional OMP algorithm, and there is about 6dB performance gain when the size of an OTFS frame is 128×16 and the normalized mean squared error (NMSE) is 0.00173. It also proves that the proposed ResNet-based channel estimation scheme can be applied to different scenarios and achieve good robustness.

OTFS channel estimation DL ResNet

其他

IEEE

1.School of Information Communication Engineering, Key Laboratory of Modern Measurement & Control Technology, Ministry of Education, Beijing Information Science and Technology University, China
2.Xi'an University of Posts & Telecommunications
3.Southern University of Science and Technology

Qingyu Li,Yi Gong,Fanke Meng,et al. Residual Learning based Channel Estimation for OTFS system[C],2022:275-280.