Low Complexity Receiver Design for Orthogonal Time Frequency Space Systems

LIAO Yong; LI Xue

doi:10.11999/JEIT230625

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LIAO Yong, LI Xue. Low Complexity Receiver Design for Orthogonal Time Frequency Space Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230625

Citation:

LIAO Yong, LI Xue. Low Complexity Receiver Design for Orthogonal Time Frequency Space Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230625

LIAO Yong, LI Xue. Low Complexity Receiver Design for Orthogonal Time Frequency Space Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230625

Citation:

LIAO Yong, LI Xue. Low Complexity Receiver Design for Orthogonal Time Frequency Space Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230625

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Low Complexity Receiver Design for Orthogonal Time Frequency Space Systems

doi: 10.11999/JEIT230625

LIAO Yong^{1, 2
,
,},
LI Xue¹

1.
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
2.
Yusu Communication Technology (Changshu) Co., Ltd, Changshu 215500, China

Funds: Chongqing Natural Science Foundation (CSTB2023NSCQ-MSX0025)

Received Date: 2023-06-25
Rev Recd Date: 2023-09-12

Available Online: 2023-09-15

Abstract

Abstract

Orthogonal Time Frequency Space (OTFS) can convert the doubly-selective channels into non-selective channels in the Delay-Doppler (DD) domain, which provides a solution for establishing reliable wireless communication in high-mobility scenarios. However, serious Inter-Doppler Interference (IDI) exists in complex multi-scattering scenarios such as internet of vehicles, which brings great challenges to the accurate demodulation of OTFS receiver signals. To solve these problems, a kind of joint Sparse Bayesian Learning (SBL) and damped Least Square Minimum Residual (d-LSMR) OTFS receiver is proposed. Firstly, based on the relationship between OTFS time domain and DD domain, the channel estimation problem is transformed into a Basis Expansion Model (BEM) to accurately estimate DD domain channels including Doppler sampling points. Then, an efficient conversion algorithm is proposed to convert the basis coefficients into channel equivalent matrix. Additionally, the noise estimated in channel estimation is used in d-LSMR equalizer, and the sparse channel matrix in DD domain is adopted to achieve fast convergence. System simulation results show that compared with the current representative OTFS receiver, the proposed scheme achieves better bit error rate performance and reduces the computational complexity.
- Orthogonal Time Frequency Space(OTFS),
- Channel estimation,
- Channel equalization,
- High-mobility scenarios,
- Sparse Bayesian learning,
- Basis Expansion Model (BEM)

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References(20)

References

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