基于基扩展模型的改进正则化正交匹配追踪V2X快时变SC-FDMA信道估计

doi:10.11959/j.issn.1000-436x.2021081

通信学报 ›› 2021, Vol. 42 ›› Issue (4): 177-184.doi: 10.11959/j.issn.1000-436x.2021081

基于基扩展模型的改进正则化正交匹配追踪V2X快时变SC-FDMA信道估计

廖勇, 蔡志镕

重庆大学微电子与通信工程学院，重庆 400044

修回日期:2020-12-16 出版日期:2021-04-25 发布日期:2021-04-01
作者简介:廖勇（1982- ），男，四川自贡人，博士，重庆大学副研究员、博士生导师，主要研究方向为下一代无线通信、人工智能、区块链及其在无线通信中的应用等。
蔡志镕（1996- ），男，福建莆田人，重庆大学硕士生，主要研究方向为无线通信信道估计算法。
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)

Basis expansion model-based improved regularized orthogonal matching pursuit channel estimation for V2X fast time-varying SC-FDMA

Yong LIAO, Zhirong CAI

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

Revised:2020-12-16 Online:2021-04-25 Published:2021-04-01
Supported by:
The National Natural Science Foundation of China(61501066);The Natural Science Foundation of Chongq-ing(cstc2019jcyj-msxmX0017)

摘要/Abstract

摘要：

为了进一步提升车联万物（V2X）的通信性能，首先根据信道冲激响应的稀疏性建立了适用于高速移动场景的基扩展模型（BEM）；其次，证明了BEM系数具有稀疏性，将信道估计问题转化为稀疏信号重构问题，进而提出基于 BEM 的改进正则化正交匹配追踪（iROMP）迭代稀疏信道估计算法（简称为 BEM-iROMP 算法）。所提算法通过iROMP获取BEM系数，利用反馈结果不断迭代以达到最优信道估计。仿真结果表明，与最小二乘法、线性最小均方误差和BEM-LS信道估计算法相比，所提算法能够有效提高V2X快时变信道下单载波频分多址系统的归一化均方误差和误码率性能。

关键词: 信道估计, 车联万物, 高速移动, 压缩感知, 基扩展模型, 正则化正交匹配追踪, 单载波频分多址

Abstract:

In order to further improve the vehicle-to-everything (V2X) communication performance of the Internet of vehicles, a basis expansion model (BEM) was adopted and suitable for high-speed mobile scenarios to transform the channel estimation into a sparse signal reconstruction.Furthermore, it was proved that the BEM coefficients were sparse, and an improved regularized orthogonal matching pursuit (iROMP) channel estimation algorithm based on BEM (BEM-iROMP) was proposed.BEM coefficients were acquired by the iROMP, and finally the feedback results were iterated to achieve the optimal channel estimation.Simulation results show that in comparison with the least square (LS), linear minimum mean squared error (LMMSE), and BEM-LS channel estimation algorithms, the proposed algorithm can effectively improve the normalized mean square error (NMSE) and bit error rate (BER) performance.

Key words: channel estimation, vehicle-to-everything, high-speed mobility, compressed sensing, basis expansion model, regularized orthogonal matching pursuit, SC-FDMA

中图分类号:

TN911.72

廖勇, 蔡志镕. 基于基扩展模型的改进正则化正交匹配追踪V2X快时变SC-FDMA信道估计[J]. 通信学报, 2021, 42(4): 177-184.

Yong LIAO, Zhirong CAI. Basis expansion model-based improved regularized orthogonal matching pursuit channel estimation for V2X fast time-varying SC-FDMA[J]. Journal on Communications, 2021, 42(4): 177-184.

图/表 7

表1

不同算法的复杂度对比"

信道估计算法	乘法运算次数	时间复杂度
LS算法	4N	O (N)
LMMSE算法	3N ³+N²	$O (N^{3})$
BEM-LS算法	4(DL)²N+DLN	$O ({(D L)}^{2} N)$
BEM-iROMP算法	4(DL)²N lg T+DLN	$O ({(D L)}^{2} N \lg T)$

表1

表2

图1

图2

图3

图4

图5

参考文献 15

[1]	马小婷, 赵军辉, 王传云 ,等. 车联网中的V2R通信[J]. 电信科学, 2016,32(8): 21-27.
	MA X T , ZHAO J H , WANG C Y ,et al. V2R communication in Internet of vehicles[J]. Telecommunications Science, 2016,32(8): 21-27.
[2]	CHEN S Z , HU J L , SHI Y ,et al. Vehicle-to-everything (V2X) services supported by LTE-based systems and 5G[J]. IEEE Communications Standards Magazine, 2017,1(2): 70-76.
[3]	WANG T , WANG X D , CUI Z P ,et al. Survey on cooperatively V2X downloading for intelligent transport systems[J]. IET Intelligent Transport Systems, 2019,13(1): 13-21.
[4]	ABBOUD K , OMAR H A , ZHUANG W H . Interworking of DSRC and cellular network technologies for V2X communications:a survey[J]. IEEE Transactions on Vehicular Technology, 2016,65(12): 9457-9470.
[5]	POPOVSKI P , TRILLINGSGAARD K F , SIMEONE O ,et al. 5G wireless network slicing for eMBB,URLLC,and mMTC:a communication-theoretic view[J]. IEEE Access, 2018,6: 55765-55779.
[6]	GANESAN K , LOHR J , MALLICK P B ,et al. NR sidelink design overview for advanced V2X service[J]. IEEE Internet of Things Magazine, 2020,3(1): 26-30.
[7]	LIAO Y , SHEN X F , SUN G D ,et al. EKF/UKF-based channel estimation for robust and reliable communications in V2V and IIoT[J]. EURASIP Journal on Wireless Communications and Networking, 2019,2019(1): 1-13.
[8]	RABBI M F , HOU S W , KO C C . High mobility orthogonal frequency division multiple access channel estimation using basis expansion model[J]. IET Communications, 2010,4(3): 353.
[9]	MA X L , GIANNAKIS G B , OHNO S . Optimal training for block transmissions over doubly selective wireless fading channels[J]. IEEE Transactions on Signal Processing, 2003,51(5): 1351-1366.
[10]	李丹, 柯峰 . 一种基于基扩展模型的 OFDM 频域快时变信道估计方法[J]. 信号处理, 2012,28(2): 193-199.
	LI D , KE F . BEM based frequency domain fast time varying channel estimation method for OFDM systems[J]. Signal Processing, 2012,28(2): 193-199.
[11]	XIE H X , GAO F F , ZHANG S ,et al. Spatial-temporal BEM and channel estimation strategy for massive MIMO time-varying systems[C]// 2016 IEEE Global Communications Conference. Piscataway:IEEE Press, 2016: 1-6.
[12]	ZUO H L , SONG H G , YUAN T P ,et al. Channel modeling and estimation in high-speed mobile environment[C]// 2016 IEEE 83rd Vehicular Technology Conference. Piscataway:IEEE Press, 2016: 1-5.
[13]	MATZ G , HLAWATSCH F . Fundamentals of time-varying communication channels[M]// Amsterdam: Elsevier, 2011.
[14]	刘翼 . 基于压缩感知的 OFDM 系统快速时变信道估计[D]. 北京:北京理工大学, 2015.
	LIU Y . Compressive estimation of fast time-varying channels in OFDM systems[D]. Beijing:Beijing Institute of Technology, 2015.
[15]	3GPP. Evolved universal terrestrial radio access (E-UTRA):user equipment (UE) radio transmission and reception (release 17):TS 36.101 V17.0.0[S]. 3rd Generation Partnership Project, 2020.

参数	数值
信道模型	EVA
载波频率/GHz	5.9
系统带宽/MHz	10
采样频率/MHz	15.36
子载波数/个	600
FFT点数/个	1 024
调制方式	QPSK

基于基扩展模型的改进正则化正交匹配追踪V2X快时变SC-FDMA信道估计

Basis expansion model-based improved regularized orthogonal matching pursuit channel estimation for V2X fast time-varying SC-FDMA

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 15

相关文章 15

Metrics

推荐阅读 0

[1]	崔伟, 于颖, 于海霞, 陈超, 李云鹏. 基于IOC-CSMP的OFDM系统稀疏信道快速重构算法[J]. 通信学报, 2023, 44(2): 52-58.
[2]	曾嵘, 杭潇. 车联网环境下可重构智能反射面辅助无线信道估计算法[J]. 通信学报, 2022, 43(8): 142-150.
[3]	任花, 牛少彰, 任如勇, 岳桢. 基于二维压缩感知的有意义图像加密算法研究[J]. 通信学报, 2022, 43(5): 45-57.
[4]	廖勇, 王世义. 高速移动环境下基于RM-Net的大规模MIMO CSI反馈算法[J]. 通信学报, 2022, 43(5): 166-176.
[5]	蒋占军, 刘欢, 张华卫, 李翠然. OTFS接收端信道估计辅助的无记忆功放非线性失真补偿算法[J]. 通信学报, 2022, 43(12): 134-145.
[6]	邢旺, 唐晓刚, 周一青, 张冲, 潘振岗. 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.
[7]	梅锴, 赵海涛, 刘潇然, 刘军, 熊俊, 任保全, 魏急波. 高效的基于数据与模型的信道估计算法[J]. 通信学报, 2022, 43(1): 59-70.
[8]	黄源, 何怡刚, 吴裕庭, 程彤彤, 隋永波, 宁暑光. 基于深度学习的压缩感知FDD大规模MIMO系统稀疏信道估计算法[J]. 通信学报, 2021, 42(8): 61-69.
[9]	吕新荣, 李有明, 国强. MIMO-OFDM系统的信道与脉冲噪声联合估计方法[J]. 通信学报, 2021, 42(12): 54-64.
[10]	傅友华, 陈栋. 混合智能反射表面结构辅助的毫米波通信信道估计[J]. 通信学报, 2021, 42(10): 189-196.
[11]	王莹, 任军, 史可, 林彬. 基于深度学习的广义频分复用系统时频双选择信道估计[J]. 通信学报, 2021, 42(10): 233-242.
[12]	黄杰,杨凡,高乙文,张博为. 超密集网络导频复用干扰避免策略[J]. 通信学报, 2020, 41(7): 165-171.
[13]	王海荣, 董健, 王玉辉. 大规模MIMO系统中基于谱分离的导频去污染[J]. 通信学报, 2020, 41(4): 197-205.
[14]	赵小虎,王刚,宋泊明,于嘉成. 基于压缩感知的设备多源信息传输与分类算法[J]. 通信学报, 2020, 41(2): 13-24.
[15]	潘作舟,孟宗,李晶,石颖. 基于自适应加速前向后向匹配追踪的压缩感知重构算法[J]. 通信学报, 2020, 41(1): 25-32.