TS-GRU-VTA：基于深度学习的车辆信道估计方案

doi:10.12305/j.issn.1001-506X.2025.09.31

Abstract

Abstract:

Under the IEEE 802.11p standard, traditional data-pilot aided （DPA） estimators struggle to effectively track time-varying channels. Although deep learning-based estimation algorithms have been widely studied, they often face issues of high complexity or poor performance. Based on this, a low-complexity channel estimation scheme that combines time-domain sampling, gated recurrent units （GRU）, and a variable coefficient temporal averaging method is proposed to effectively reduce complexity and enhance performance. Simulation results demonstrate that this algorithm outperforms comparative algorithms in both performance and complexity.

Key words: low-complexity channel estimation, time-domain sapling, deep learning, gated recurrent unit (GRU) network, variable coefficient temporal averaging

CLC Number:

TN 911.72

Ce JI, Xiangyu MA, Xiaoyu MU, Jiayi ZHAO. TS-GRU-VTA: vehicle channel estimation scheme based on deep learning[J]. Systems Engineering and Electronics, 2025, 47(9): 3093-3098.

Figures/Tables 9

Fig.1

Fig.2

Fig.3

Table 1

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

References 15

1	JIANG D, DELGROSSI L. IEEE 802.11p: towards an international standard for wireless access in vehicular environments[C] // Proc. of the IEEE Conference on Vehicular Technology, 2008.
2	KYUNBYOUNG K, HANHO W. Noise-canceling channel estimation schemes based on the cir length estimation for IEEE 802.11p/OFDM systems[J]. Electronics, 2024, 13 (6): 1110. doi: 10.3390/electronics13061110
3	FERNANDEZ J, BORRIES K, CHENG L, et al. Performance of the 802.11p physical layer in vehicle-to-vehicle environments[J]. IEEE Trans. on Vehicular Technology, 2012, 61 (1): 3- 14. doi: 10.1109/TVT.2011.2164428
4	WANG T, HUSSAIN A, CAO Y, et al. An improved channel estimation technique for IEEE 802.11p standard in vehicular communications[J]. Sensors, 2019, 19 (1): 98.
5	KYUNBYOUNG K, HANHO W. The channel estimation based on FSC method for IEEE 802.11p OFDM system[C] // Proc. of the 14th International Conference on Information and Communication Technology Convergence, 2023.
6	HAN S H, PARK J S, SONG C. Virtual subcarrier aided channel estimation schemes for tracking rapid time variant channels in IEEE 802.11p systems[C] // Proc. of the IEEE Conference on Vehicular Technology, 2020.
7	GIZZINI A K, CHAFII M. Low complex methods for robust channel estimation in doubly dispersive environments[J]. IEEE Access, 2022, 10, 34321- 34339. doi: 10.1109/ACCESS.2022.3162928
8	桂冠, 王禹, 黄浩. 基于深度学习的物理层无线通信技术: 机遇与挑战[J]. 通信学报, 2019, 40 (2): 19- 23.
	GUI G, WANG Y, HUANG H. Deep learning-based wireless communication technologies at the physical layer: opportunities and challenges[J]. Journal of Communications, 2019, 40 (2): 19- 23.
9	孟帆. 基于深度学习的无线通信物理层关键技术研究[D]. 南京: 东南大学, 2020.
	MENG F. Research on key techniques of deep learning-based wireless physical layer [D]. Nanjing: Southeast University, 2020.
10	季策, 宋博翰, 耿蓉, 等. 快时变信道下基于深度学习的OFDM系统信道估计[J]. 系统工程与电子技术, 2023, 45 (11): 3649- 3655.
	JI C, SONG B H, GENG R, et al. Deep learning based channel estimation for OFDM systems in fast time-varying channel[J]. Systems Engineering and Electronics, 2023, 45 (11): 3649- 3655.
11	HAN S H, OH Y J, SONG C. A deep learning based channel estimation scheme for IEEE 802.11p systems[C] // Proc. of the IEEE International Conference on Communications, 2019.
12	PAN J, SHAN H G, LI R P, et al. Channel estimation based on deep learning in vehicle-to-everything environments[J]. IEEE Communication Latter, 2021, 25 (6): 1891- 1895. doi: 10.1109/LCOMM.2021.3059922
13	GIZZINI A K, CHAFII M, EHSANFAR S, et al. Temporal averaging LSTM-based channel estimation scheme for IEEE 802.11p standard[C] // Proc. of the IEEE Global Communnication Conference, 2021.
14	HOU J, LIU H J, ZHANG Y, et al. GRU-based deep learning channel estimation scheme for the IEEE 802.11p standard[J]. IEEE Wireless Communications Letters, 2023, 12 (5): 764- 768. doi: 10.1109/LWC.2022.3187110
15	ACOST M, GUILLERMO, WEIT N, et al. Six time and frequency-selective empirical channel models for vehicular wireless LANs[J]. IEEE Vehicular Technology Magazine, 2007, 2 (4): 4- 11. doi: 10.1109/MVT.2008.917435

参数名称	配置
隐藏层规格	48
训练迭代次数	500
批数据大小	128
优化器	Adam
训练，测试样本数	12000，2000
学习率	0.001
训练信噪比/dB	40

[1]	Kaiming LI, Yuanpeng ZHANG, Ying LUO, Xiaonan DAI. Research progress on radar target recognition of ballistic missile [J]. Systems Engineering and Electronics, 2025, 47(9): 2870-2889.
[2]	Xiaowei YAN, Chong LING, Shengbin SHI. Design and implementation of a rapid detection system for surface unexploded submunitions [J]. Systems Engineering and Electronics, 2025, 47(8): 2639-2645.
[3]	Wanying ZHANG, Youbing GAO, Zeyi LI, Pengfei LI, Wei ZHANG. Background signal suppression algorithm based on dual-path feature fusion net [J]. Systems Engineering and Electronics, 2025, 47(7): 2406-2413.
[4]	Xinzheng ZHANG, Mengke YAN, Xiaolin ZHU. Noise pseudo-label tolerant semi-supervised SAR target recognition [J]. Systems Engineering and Electronics, 2025, 47(6): 1796-1805.
[5]	Zhijie JIANG, Heng SONG, Nan HU, Lanxi DUAN, Ping CAO. Target recognition and classification algorithm of MMW radar in tunnel [J]. Systems Engineering and Electronics, 2025, 47(5): 1453-1460.
[6]	Qianqi NIE, Minghui SHA, Yingshen ZHU, Chongyu WANG, Nianqiang CUI. Radar multi-component signal recognition method based on blind source separation combined with singular spectrum analysis [J]. Systems Engineering and Electronics, 2025, 47(4): 1168-1175.
[7]	Lei YUAN, Yanjuan YANG, Yi GUO, Peng DAI. Deep learning-based semi-blind identification algorithm for code rates of LDPC codes with correlated noise [J]. Systems Engineering and Electronics, 2025, 47(4): 1335-1345.
[8]	Xiarun SHEN, Ruonan LI, Haotian ZHANG. Server KPI anomaly detection based on CVAE-LSTM [J]. Systems Engineering and Electronics, 2025, 47(3): 1019-1027.
[9]	Xiaolin LIU, Mengjiao GUO, Zhuo LI. Adaptive graph convolutional recurrent network prediction method for flight delay based on Dueling DQN optimization [J]. Systems Engineering and Electronics, 2025, 47(2): 568-579.
[10]	Weihong FU, Xinyu ZHANG, Naian LIU. Single-channel blind source separation algorithm for co-frequency and co-modulation based on multi-scale fusion neural network [J]. Systems Engineering and Electronics, 2025, 47(2): 641-649.
[11]	Wei CAI, Xin WANG, Xinhao JIANG, Zhiyong YANG, Dong CHEN. Research on few shot target detection method based on decoupling [J]. Systems Engineering and Electronics, 2024, 46(9): 2941-2950.
[12]	Xiaoxuan CHEN, Shuwen XU, Shaohai HU, Xiaole MA. Infrared and visible light image fusion based on convolution and self attention [J]. Systems Engineering and Electronics, 2024, 46(8): 2641-2649.
[13]	Qianglong WANG, Xiaoguang GAO, Bicong WU, Zijian HU, Kaifang WAN. Review of research on restricted Boltzmann machine and its variants [J]. Systems Engineering and Electronics, 2024, 46(7): 2323-2345.
[14]	Xiantao SUN, Wangyang JIANG, Wenjie CHEN, Weihai CHEN, Yali ZHI. Object grasp pose detection based on the region of interest [J]. Systems Engineering and Electronics, 2024, 46(6): 1867-1877.
[15]	Xuemei CHEN, Zhiheng LIU, Suiping ZHOU, Hang YU, Yanming LIU. Road extraction from high-resolution remote sensing images based on HRNet [J]. Systems Engineering and Electronics, 2024, 46(4): 1167-1173.

TS-GRU-VTA: vehicle channel estimation scheme based on deep learning

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 15

Related Articles 15

Recommended Articles

Metrics

Comments