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

Abstract

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

CLC Number:

TN911.72

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.

Figures/Tables 7

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	$O ({(D L)}^{2} N \lg T)$

References 15

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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

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

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