Name
Abstract | ||
|---|---|---|
Orthogonal time frequency space (OTFS) modu-lation is a new waveform modulation technique which is able to resist the Doppler shift in high-mobility scenario by con-verting a fast time-varying channel in the time-frequency (TF) domain into a time-invariant channel in the delay-Doppler (DD) domain. However, the dimension of the equivalent channel matrix of the OTFS system is usually large, resulting in an excellent challenge for OTFS signal detection. This paper proposes a model-driven intelligent detection method. It first modifies the original orthogonal approximate message passing (OAMP) by constructing several trainable parameters. Then, the model-driven deep learning technology is utilized to train these parameters to improve the convergence and detection accuracy of the method. The experiment results show that the proposed method has better BER performances than some traditional state-of-the-art algorithms. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICCCWorkshops55477.2022.9896698 | 2022 IEEE/CIC International Conference on Communications in China (ICCC Workshops) |
Keywords | DocType | ISSN |
Orthogonal time frequency space (OTF-S),Model-driven,Orthogonal approximate message passing (OAMP) | Conference | 2474-9133 |
ISBN | Citations | PageRank |
978-1-6654-5978-5 | 0 | 0.34 |
References | Authors | |
11 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Ding | 1 | 0 | 0.34 |
| Shuo Li | 2 | 0 | 0.34 |
| Xufan Zhang | 3 | 0 | 0.34 |
| Qijiang Yuan | 4 | 0 | 0.34 |
| Lixia Xiao | 5 | 0 | 0.34 |