Low Complexity Receiver Design for Orthogonal Time Frequency Space Systems

LIAO Yong; LI Xue

doi:10.11999/JEIT230625

Volume 46 Issue 6

Jun. 2024

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LIAO Yong, LI Xue. Low Complexity Receiver Design for Orthogonal Time Frequency Space Systems[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2418-2424. 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, 2024, 46(6): 2418-2424. doi: 10.11999/JEIT230625

LIAO Yong, LI Xue. Low Complexity Receiver Design for Orthogonal Time Frequency Space Systems[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2418-2424. 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, 2024, 46(6): 2418-2424. doi: 10.11999/JEIT230625

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Low Complexity Receiver Design for Orthogonal Time Frequency Space Systems

doi: 10.11999/JEIT230625

LIAO Yong^,,
LI Xue

School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China

Funds: Chongqing Natural Science Foundation (CSTB2023NSCQ-MSX0025)

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

Available Online: 2023-09-15

Publish Date: 2024-06-30

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

[1]	YUAN Weijie, LI Shuangyang, WEI Zhiqiang, et al. New delay Doppler communication paradigm in 6G era: A survey of orthogonal time frequency space (OTFS)[J]. China Communications, 2023, 20(6): 1–25. doi: 10.23919/JCC.fa.2022-0578.202306.
[2]	邢旺, 唐晓刚, 周一青, 等. 面向OTFS的时延-多普勒域信道估计方法综述[J]. 通信学报, 2022, 43(12): 188–201. doi: 10.11959/j.issn.1000-436x.2022224. XING Wang, TANG Xiaogang, ZHOU Yiqing, et al. Survey of channel estimation method in delay-Doppler domain for OTFS[J]. Journal on Communications, 2022, 43(12): 188–201. doi: 10.11959/j.issn.1000-436x.2022224.
[3]	WEI Zhiqiang, YUAN Weijie, LI Shuangyang, et al. Orthogonal time-frequency space modulation: A promising next-generation waveform[J]. IEEE Wireless Communications, 2021, 28(4): 136–144. doi: 10.1109/MWC.001.2000408.
[4]	LIAO Yong and LI Xue. Joint multi-domain channel estimation based on sparse Bayesian learning for OTFS system[J]. China Communications, 2023, 20(1): 14–23. doi: 10.23919/JCC.2023.01.002.
[5]	WU Yiyan and ZOU W Y. Orthogonal frequency division multiplexing: A multi-carrier modulation scheme[J]. IEEE Transactions on Consumer Electronics, 1995, 41(3): 392–399. doi: 10.1109/30.468055.
[6]	蒋占军, 刘庆达, 张鈜, 等. 高速移动通信系统中OTFS分数多普勒信道估计加窗研究[J]. 电子与信息学报, 2022, 44(2): 646–653. doi: 10.11999/JEIT210561. JIANG Zhanjun, LIU Qingda, ZHANG Hong, et al. Study on OTFS fractional Doppler channel estimation and windowing in high-speed mobile communication systems[J]. Journal of Electronics &Information Technology, 2022, 44(2): 646–653. doi: 10.11999/JEIT210561.
[7]	WEI Zhiqiang, YUAN Weijie, LI Shuangyang, et al. Transmitter and receiver window designs for orthogonal time-frequency space modulation[J]. IEEE Transactions on Communications, 2021, 69(4): 2207–2223. doi: 10.1109/TCOMM.2021.3051386.
[8]	WEI Zhiqiang, YUAN Weijie, LI Shuangyang, et al. Performance analysis and window design for channel estimation of OTFS modulation[C]. 2021 IEEE International Conference on Communications, Montreal, Canada, IEEE, 2021: 1–7.
[9]	RAMACHANDRAN M K and CHOCKALINGAM A. MIMO-OTFS in high-Doppler fading channels: Signal detection and channel estimation[C]. 2018 IEEE Global Communications Conference, Abu Dhabi, United Arab Emirates, IEEE, 2018: 206–212.
[10]	RAVITEJA P, PHAN K T, and HONG Yi. Embedded pilot-aided channel estimation for OTFS in delay–Doppler channels[J]. IEEE Transactions on Vehicular Technology, 2019, 68(5): 4906–4917. doi: 10.1109/TVT.2019.2906357.
[11]	QU Huiyang, LIU Guanghui, ZHANG Lei, et al. Low-dimensional subspace estimation of continuous-Doppler-spread channel in OTFS systems[J]. IEEE Transactions on Communications, 2021, 69(7): 4717–4731. doi: 10.1109/TCOMM.2021.3072744.
[12]	SURABHI G D and CHOCKALINGAM A. Low-complexity linear equalization for OTFS modulation[J]. IEEE Communications Letters, 2020, 24(2): 330–334. doi: 10.1109/LCOMM.2019.2956709.
[13]	THAJ T and VITERBO E. Low complexity iterative rake decision feedback equalizer for zero-padded OTFS systems[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 15606–15622. doi: 10.1109/TVT.2020.3044276.
[14]	ZEMEN T, BERNADO L, CZINK N, et al. Iterative time-variant channel estimation for 802.11p using generalized discrete prolate spheroidal sequences[J]. IEEE Transactions on Vehicular Technology, 2012, 61(3): 1222–1233. doi: 10.1109/TVT.2012.2185526.
[15]	游康勇, 杨立山, 刘玥良, 等. 基于稀疏贝叶斯学习的网格自适应多源定位[J]. 电子与信息学报, 2018, 40(9): 2150–2157. doi: 10.11999/JEIT171238. YOU Kangyong, YANG Lishan, LIU Yueliang, et al. Adaptive grid multiple sources localization based on sparse Bayesian learning[J]. Journal of Electronics &Information Technology, 2018, 40(9): 2150–2157. doi: 10.11999/JEIT171238.
[16]	FONG D C L and SAUNDERS M. LSMR: An iterative algorithm for sparse least-squares problems[J]. SIAM Journal on Scientific Computing, 2011, 33(5): 2950–2971. doi: 10.1137/10079687X.
[17]	HONG Linyi and ZHANG Naimin. On the preconditioned MINRES method for solving singular linear systems[J]. Computational and Applied Mathematics, 2022, 41(7): 304. doi: 10.1007/s40314-022-02007-w.
[18]	DANAEI K, MORADZADEH A, NOROUZI G H, et al. 3D inversion of gravity data with unstructured mesh and least-squares QR-factorization (LSQR)[J]. Journal of Applied Geophysics, 2022, 206: 104781. doi: 10.1016/j.jappgeo.2022.104781.
[19]	GOLUB G and KAHAN W. Calculating the singular values and pseudo-inverse of a matrix[J]. Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis, 1965, 2(2): 205–224. doi: 10.1137/0702016.
[20]	3GPP TS 36.101 (V17.0. 0) Technical specification group radio access network; Evolved universal terrestrial radio access (E-UTRA); User equipment (UE) radio transmission and reception[S]. 2020.