基于历史信息的软卡尔曼滤波迭代时变信道估计方法

doi:10.11959/j.issn.1000-0801.2020038

Abstract

Abstract:

For high-speed mobile MIMO-OFDM systems,a historical information based iterative soft-Kalman filter time-varying channel estimation method was proposed.Considering that the channels experienced by different trains in the high-speed railway environment have strong correlation,the channel information of the historical train was firstly used to obtain the optimal basis function,which can be employed to model the channel.By the optimal basis function,the computational complexity was reduced and the channel estimation accuracy was improved for the proposed method.Secondly,the soft-Kalman filter and data detection were jointed to estimate the base coefficient in each iteration.To reduce the effect of data detection error propagation on the channel estimation,the soft data detection scheme was employed and the soft detection error was treated as noise in each iteration.In addition,the soft-Kalman filter used in the proposed method does not involve the AR model tracking factor,thereby avoiding the computational complexity introduced by the estimated tracking factor.The simulation results show that the proposed method has better estimation performance,and is more suitable for time-varying channel acquisition of actual high-speed mobile scenarios.

Key words: MIMO-OFDM, high-speed mobility, time-varying channel estimation, historical information, soft-Kalman filter, iterative channel estimation

CLC Number:

TN929

Lu CHENG,Lihua YANG,Zenghao WANG,Jie ZHANG,Yan LIANG. Historical information based iterative soft Kalman time-varying channel estimation method[J]. Telecommunications Science, 2020, 36(9): 23-31.

Figures/Tables 4

信道估计方法	总计算复杂度
参考文献[7]	$N^{2} L + 2 N_{p} Q^{2} + N_{p} Q + o (Q^{3})$
参考文献[6]	$L [4 N Q^{2} + N^{2} Q + Q^{3} + 2 o (Q^{3})] + 2 N [Q^{2} L N (8 N - 1) + 3]$
本文所提方法	$N^{2} L + [4 N Q^{2} + N^{2} Q + Q^{3} + 2 o (Q^{3})] + 2 N [Q^{2} L N (8 N - 1) + 3]$

References 20

[1]	HU Y , LI H , CHANG Z ,et al. Scheduling strategy for multimedia heterogeneous high-speed train networks[J]. IEEE Transactions on Vehicular Technology, 2017,66(4): 3265-3279.
[2]	SUN N , ZHAO Y , SUN L ,et al. Distributed and dynamic resource management for wireless service delivery to high-speed trains[J]. IEEE Access, 2017(5): 620-632.
[3]	TANG Q , LONG H , YANG H J ,et al. An enhanced LMMSE channel estimation under high speed railway scenarios[C]// Proceedings of 2017 IEEE International Conference on Communications Workshops. Piscataway:IEEE Press, 2017: 999-1004.
[4]	LIAO Y , SUN G D , SHEN X F ,et al. BEM-based channel estimation and interpolation methods for doubly-selective OFDM channel[C]// Proceedings of 2018 IEEE International Conference on Smart Internet of Things. Piscataway:IEEE Press, 2018: 70-75.
[5]	REN X , TAO M X , CHEN W . Compressed channel estimation with position-based ICI elimination for high-mobility SIMO-OFDM systems[J]. IEEE Transactions on Vehicular Technology, 2015,65(8): 6204-6216.
[6]	YANG L H , TANG L X , LIANG Y . Iterative channel estimation for MIMO-OFDM system in fast time-varying channels[J]. KSII Transactions on Internet and Information Systems, 2016,66(5): 4240-4258.
[7]	WANG X Y , WANG G P , SUN J ,et al. Channel estimation with new basis expansion model for wireless communications on hign speed railways[C]// Proceedings of 2016 IEEE 83rd Vehicular Technology Conference. Piscataway:IEEE Press, 2016: 1-5.
[8]	YE H , LI G Y , JUANG B H . Power of deep learning for channel estimation and signal detection in OFDM systems[J]. IEEE Wireless Communications Letters, 2018,7(1): 114-117.
[9]	CHUN C J , KANG J M , KIM I M . Deep learning based joint pilot design and channel estimation for multiuser MIMO channels[J]. IEEE Communications Letters, 2019,23(11): 1999-2003.
[10]	MA X L , YE H , LI Y . Learning assisted estimation for time-varying channel[C]// Proceedings of 2018 15th International Symposium on Wireless Communication Systems (ISWCS).[S.l.:s.n]. 2018, 28-31.
[11]	YANG Y W . Deep learning-based channel estimation for doubly selective fading channels[J]. IEEE Access, 2019(7): 36579-36589.
[12]	ZHAO Y R , WANG X Y , WANG G P ,et al. Channel estimation and throughput evalution for 5G wireless communication systems in various scenarios on high speed railways[J]. China Communication, 2018,15(4): 86-97.
[13]	WANG X Y , WANG G P , FAN G F ,et al. Channel estimation with expectarion maximization and historical information based basis expansion model for wireless communication systems on high speed railway[J]. IEEE Access, 2018(6): 72-80.
[14]	HE R S , ZHONG Z D , AI B ,et al. Measurements and analysis of short-term fading behavior for high-speed rail viaduct scenario[C]// Proceedings of 2012 IEEE International Conference on Communications. Piscataway:IEEE Press, 2012: 4563-4567.
[15]	MIN C K , CHANG N , CHA J S . MIMO-OFDM downlink channel prediction for IEEE 802.16e systems using Kalman filter[C]// Proceedings of 2007 IEEE Wireless Communications and Networking Conference. Piscataway:IEEE Press, 2007.
[16]	CHEN B S , YANG C Y , LIAO W J . Robust fast time-varying multipath fading channel estimation and equalization for MIMO-OFDM systems via a fuzzy method[J]. IEEE Transactions on Vehicular Technology, 2012,61(4): 1599-1609.
[17]	KIM J Y , PARK B , HANG D . Robust channel estimation in a mobile MIMO OFDM system[C]// Proceedings of 2004 IEEE 59th Vehicular Technology Conference. Piscataway:IEEE Press, 2004: 594-597.
[18]	ZHAO N , YU F R , SUN H J ,et al. Interference alignment with delayed channel state information and dynamic AR-model channel prediction in wireless networks[J]. Wireless Networks, 2015,21(4): 1227-1242.
[19]	NATORI T , TANABE N , FURUKAWA T . A MIMO-OFDM channel estimation algorithm for high-speed movement environments[C]// Proceedings of 2014 6th International Symposium on Communications,Control and Signal Processing. Piscataway:IEEE Press, 2014: 348-351.
[20]	SIMON E P , KHALIGHI M A . Iterative soft-kalman channel estimation for fast time-varying MIMO-OFDM channels[J]. IEEE Wireless Communications Letters, 2015,2(6): 599-602.

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