Abstract
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.
Original language | English |
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Title of host publication | 2022 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2022 |
Publisher | IEEE |
Pages | 320-324 |
Number of pages | 5 |
ISBN (Electronic) | 9781665459778 |
ISBN (Print) | 9781665459785 |
DOIs | |
Publication status | Published - 2022 |
Externally published | Yes |
Event | 2022 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2022 - Sanshui, Foshan, China Duration: 11 Aug 2022 → 13 Aug 2022 |
Conference
Conference | 2022 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2022 |
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Country/Territory | China |
City | Sanshui, Foshan |
Period | 11/08/22 → 13/08/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Funding
This paper is supported by the National Natural Science Foundation of China under Project 62101232 and by the Natural Science Foundation of Guangdong Province under Grant 2022A1515011257.
Keywords
- channel estimation
- convolutional residual neural network
- data denoising
- orthogonal time frequency space (OTFS)