Time-varying channel estimation method based on sliding window filtering and polynomial fitting

doi:10.11772/j.issn.1001-9081.2020122035

Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (9): 2699-2704.DOI: 10.11772/j.issn.1001-9081.2020122035

Special Issue: 网络与通信

• Network and communications • Previous Articles Next Articles

Time-varying channel estimation method based on sliding window filtering and polynomial fitting

JING Xinghong¹, SUN Guodong², HE Shibiao¹, LIAO Yong²

1. School of Electronic Information, Chongqing Institute of Engineering, Chongqing 400056, China;
2. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China

Received:2020-12-25 Revised:2021-03-05 Online:2021-05-13 Published:2021-09-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61501066), the Natural Science Foundation of Chongqing (cstc2019jcyj-msxmX0017).

基于滑窗滤波和多项式拟合的时变信道估计方法

景兴红¹, 孙国栋², 何世彪¹, 廖勇²

1. 重庆工程学院电子信息学院, 重庆 400056;
2. 重庆大学微电子与通信工程学院, 重庆 400044

通讯作者: 廖勇
作者简介:景兴红(1984-),重庆人,男,副教授,硕士,主要研究方向:移动通信;孙国栋(1996-),男,安徽宣城人,硕士研究生,主要研究方向:高速移动场景下的信道估计;何世彪(1963-),男,安徽安庆人,教授,博士,主要研究方向:宽带无线通信、智能信号与信息处理;廖勇(1982-),男,四川自贡人,副研究员,博士,CCF杰出会员,主要研究方向:移动通信、人工智能。
基金资助:
国家自然科学基金资助项目（61501066）；重庆市自然科学基金资助项目（cstc2019jcyj-msxmX0017）。

Abstract

Abstract: The Long Term Evolution based Vehicle to Everything (LTE-V2X) standard follows the LTE standard's frame format and uses a block-type pilot assisted Single-Carrier Frequency-Division Multiple Access (SC-FDMA) system for channel estimation. However, due to the time-varying characteristics of the V2X channel, large technical challenges are brought to the channel estimation at the receiver. Therefore, a time-varying channel estimation method based on sliding window filtering and polynomial fitting was designed. Aiming at the noise problem at pilot symbols, based on Least Squares (LS) method, an adaptive-length sliding window filtering was adopted for noise reduction, so as to ensure the channel estimation accuracy of pilot symbols. Furthermore, according to the size of the Doppler frequency shift of data symbols, an adaptive-order polynomial fitting method was designed to track the channel changes at data symbols. The simulation results show that the proposed method has a good denoising effect based on LS method. In the case of low-speed movement, the estimation accuracy of the proposed method is between those of LS method and Linear Minimum Mean Square Error (LMMSE) method. In the case of high-speed movement, the proposed method can fit the time-varying channel characteristics better, and its performance exceeds that of the channel estimation method of LMMSE method combined with linear interpolation. The above results show that the proposed method has better adaptability than the comparison methods and is suitable for LTE-V2X communication scenarios with different channel noises and terminal moving speeds.

Key words: channel estimation, time-varying channel, Single-Carrier Frequency-Division Multiple Access (SC-FDMA), sliding window filtering, polynomial fitting

摘要： 基于长期演进（LTE）的车辆到一切（LTE-V2X）标准沿用LTE标准的帧格式，并采用块状导频辅助的单载波频分多址（SC-FDMA）系统完成信道估计。然而，由于V2X信道的时变特性，接收机信道估计面对巨大的技术挑战。因此，设计了一种基于滑窗滤波和多项式拟合的时变信道估计方法。针对导频符号处的噪声问题，在最小二乘（LS）方法基础上，采用了自适应长度的滑动窗口滤波进行降噪处理，从而保证导频符号处的信道估计精度。另外，根据多普勒频移大小，设计了自适应阶次的多项式拟合方法来跟踪数据符号处的信道变化。仿真结果表明，所提方法在LS方法的基础上有良好的去噪效果，在低速移动情况下的估计精度介于LS方法和线性最小均方误差（LMMSE）方法之间，而该方法在高速移动条件下能更好地拟合信道的时变特性，且性能上超过了LMMSE方法结合线性插值的信道估计方法。以上结果说明，所提方法相对于对比方法具有更好的自适应性，适用于不同的信道噪声和终端移动速度下的LTE-V2X通信场景。

关键词: 信道估计, 时变信道, 单载波频分多址, 滑动窗口滤波, 多项式拟合

CLC Number:

TN929.5

JING Xinghong, SUN Guodong, HE Shibiao, LIAO Yong. Time-varying channel estimation method based on sliding window filtering and polynomial fitting[J]. Journal of Computer Applications, 2021, 41(9): 2699-2704.

景兴红, 孙国栋, 何世彪, 廖勇. 基于滑窗滤波和多项式拟合的时变信道估计方法[J]. 计算机应用, 2021, 41(9): 2699-2704.

References

[1] LV Z H,LOU R R,SINGH A K. AI empowered communication systems for intelligent transportation systems[J]. IEEE Transactions on Intelligent Transportation Systems,2021,22(7):4579-4587.
[2] SODHRO A H,OBAIDAT M S,ABBASI Q H,et al. Quality of service optimization in an IoT-driven intelligent transportation system[J]. IEEE Wireless Communications,2019,26(6):10-17.
[3] 李家印, 郭文忠, 李小燕, 等. 基于智能交通的隐私保护道路状态实时监测方案[J]. 通信学报,2020,41(7):73-83.(LI J Y, GUO W Z, LI X Y, et al. Privacy-preserving real-time road conditions monitoring scheme based on intelligent traffic[J]. Journal on Communications,2020,41(7):73-83.)
[4] LIAO Y,SUN G D,CAI Z R,et al. Nonlinear Kalman filter-based robust channel estimation for high mobility OFDM systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2020(Early Access):1-13.
[5] ANAND K,GUAN Y L,LIU X B,et al. Pilot design for BEMbased channel estimation in doubly selective channel[J]. IEEE Transactions on Vehicular Technology,2020,69(2):1679-1694.
[6] MOLINA-MASEGOSA R, GOZALVEZ J, SEPULCRE M. Comparison of IEEE 802.11p and LTE-V2X:an evaluation with periodic and aperiodic messages of constant and variable size[J]. IEEE Access,2020,8:121526-121548.
[7] ANWAR W, FRANCHI N, FETTWEIS G. Physical layer evaluation of V2X communications technologies:5G NR-V2X, LTE-V2X, IEEE 802. (11bd, and IEEE 802.11p[C]//Proceedings of the IEEE 90th Vehicular Technology Conference. Piscataway:IEEE,2019:1-7.)
[8] FERNANDEZ J A,BORRIES K,CHENG L,et al. Performance of the 802.11p physical layer in vehicle-to-vehicle environments[J]. IEEE Transactions on Vehicular Technology,2012,61(1):3-14.
[9] ZHAO Z J,CHENG X,WEN M W,et al. Channel estimation schemes for IEEE 802.11p standard[J]. IEEE Intelligent Transportation Systems Magazine,2013,5(4):38-49.
[10] 王赛飞. LTE-V2X标准下基于导频的信道估计方法[J]. 工业控制计算机,2019,32(8):62-63,80.(WANG S F. A pilot based channel estimation method under LTE-V2X standard[J]. Industrial Control Computer,2019,32(8):62-63,80.)
[11] LEE J,MOON S,KWON S,et al. Adaptive channel estimation scheme based on LTE uplink in V2V environments[J]. Wireless Personal Communications,2018,102(1):383-398.
[12] QU H Y,LIU G H,WANG Y Y,et al. Time-domain channel estimation for the LTE-V system over high-speed mobile channels[C]//Proceedings of the 2018 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting. Piscataway:IEEE,2018:1-5.
[13] 裴圆丽. LTE/LTE-Advanced上行链路SC-FDMA技术研究[D]. 南京:南京邮电大学,2011:13-14.(PEI Y L. Research on SCFDMA technology for LTE/LTE-Advanced uplink[D]. Nanjing:Nanjing University of Posts and Telecommunications, 2011:13-14.)
[14] 张秀艳, 刘珈池. 基于ZC序列的OFDM系统定时同步改进算法[J]. 吉林大学学报(信息科学版),2019,37(6):610-616. (ZHANG X Y, LIU J C. Improved timing synchronization algorithm for OFDM system based on ZC sequence[J]. Journal of Jilin University(Information Science Edition),2019,37(6):610-616.)
[15] 刘剑锋, 李瑞华, 刘垚圻, 等. 无线通信中的信噪比估计算法研究[J]. 计算机工程与应用,2020,56(18):82-89.(LIU J F,LI R H,LIU Y Q,et al. Research on signal-to-noise ratio estimation algorithm in wireless communication[J]. Computer Engineering and Applications,2020,56(18):82-89.)
[16] WEI R Y,MA X C,ZHAO S D,et al. Doppler estimation based on dual-HFM signal and speed spectrum scanning[J]. IEEE Signal Processing Letters,2020,27:1740-1744.
[17] 陈卓立. LTE-A系统下行链路MIMO检测的研究与实现[D]. 成都:电子科技大学,2014:24-26.(CHEN Z L. Research and implementation of LTE-A system downlink MIMO detection[D]. Chengdu:University of Electronic Science and Technology of China,2014:24-26.)

Time-varying channel estimation method based on sliding window filtering and polynomial fitting

基于滑窗滤波和多项式拟合的时变信道估计方法

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