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Superimposed Perfect Binary Array-Aided Channel Estimation for OTFS Systems.
Tang, Zuping; Kong, Hengyou; Wu, Ziyu; Wei, Jiaolong.
Afiliação
  • Tang Z; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Kong H; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Wu Z; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Wei J; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.
Entropy (Basel) ; 25(8)2023 Aug 03.
Article em En | MEDLINE | ID: mdl-37628193
ABSTRACT
Orthogonal time-frequency space (OTFS) modulation outperforms orthogonal frequency-division multiplexing in high-mobility scenarios through better channel estimation. Current superimposed pilot (SP)-based channel estimation improves the spectral efficiency (SE) when compared to that of the traditional embedded pilot (EP) method. However, it requires an additional non-superimposed EP delay-Doppler frame to estimate the delay-Doppler taps for the following SP-aided frames. To handle this problem, we propose a channel estimation method with high SE, which superimposes the perfect binary array (PBA) on data symbols as the pilot. Utilizing the perfect autocorrelation of PBA, channel estimation is performed based on a linear search to find the correlation peaks, which include both delay-Doppler tap information and complex channel gain in the same superimposed PBA frame. Furthermore, the optimal power ratio of the PBA is then derived by maximizing the signal-to-interference-plus-noise ratio (SINR) to optimize the SE of the proposed system. The simulation results demonstrate that the proposed method can achieve a similar channel estimation performance to the existing EP method while significantly improving the SE.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article