Deep Residual Learning for OTFS Channel Estimation with Arbitrary Noise

Xiaoqi Zhang1; Weijie Yuan1; Chang Liu2

10.1109/ICCCWorkshops55477.2022.9896721

2022

2474-9133

978-1-6654-5978-5

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

320-324

11-13 Aug. 2022

Sanshui, Foshan, China

Orthogonal time frequency space (OTFS) modu-lation has proved its capability of achieving significant error performance advantages over orthogonal frequency division mul-tiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS channel estimation is that the performance of model-based estimators will drop dramatically in the scenarios with unknown and burst noise. In this paper, we model the channel estimation as a denoising problem and adopt a deep residual denoising network (DRDN) approach to implicitly learn the residual noise for recovering the channel matrix. Different from existing model-based channel estimators which only work well under white Gaussian noise, our proposed DRDN-based method is able to handle arbitrary noise, including both the correlated Gaussian noise and the non-Gaussian noise (e.g., t-distribution noise) cases. Finally, our simulations verify the effectiveness of the proposed OTFS channel estimation approach in arbitrary noise environments.

orthogonal time frequency space (OTFS) channel estimation convolutional residual neural network data denoising

第一

IEEE

1.Southern University of Science and Technology, Shenzhen, China
2.University of New South Wales, Sydney, Australia

Xiaoqi Zhang,Weijie Yuan,Chang Liu. Deep Residual Learning for OTFS Channel Estimation with Arbitrary Noise[C],2022:320-324.