快时变信道下基于深度学习的OFDM系统信道估计

doi:10.12305/j.issn.1001-506X.2023.11.33

Abstract

Abstract:

In order to solve the problem of poor channel estimation performance caused by non-stationary characteristics of fast time-varying channels, a channel estimation algorithm based on deep learning under basis expansion model is proposed and applied to orthogonal frequency division multiplexing (OFDM) systems. Firstly, according to the local correlation characteristics of the fast time-varying channel matrix, a time-frequency feature extraction network is constructed to extract the relevant features of the channel in time domain and frequency domain by using the convolution structure, and is embedded in the next network for feature fusion. Secondly, the gated recurrent unit is used to capture the time correlation of channel changes at different symbols, so as to achieve more accurate channel estimation in the fast time-varying channel environment. Simulation results show that compared with other channel estimation algorithms in fast time-varying environments, the performance of the proposed algorithm is improved obviously. Meanwhile, the lightweight structure of the network reduces the complexity of the algorithm by at least 20%.

Key words: channel estimation, northogonal frequecy division multiplexing (OFDM), fast time-varing, deep learning, basis expansion model

CLC Number:

TN911.72

Ce JI, Bohan SONG, Rong GENG, Minjun LIANG. Deep learning based channel estimation for OFDM systems in fast time-varying channel[J]. Systems Engineering and Electronics, 2023, 45(11): 3649-3655.

Figures/Tables 8

Fig.1

Table 1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Table 2

References 17

1	SIMKO M, MEH C, WRULICH M, et al. Doubly dispersive channel estimation with scalable complexity[C]//Proc. fo the International Itg Workshop on Smart Antennas, 2010: 251-256.
2	ZHANG J L, WEN H, SONG H H, et al. Using basis expansion model for physical layer authentication in time-variant system[C]//Proc. of the IEEE Conference on Communications and Network Security, 2016: 348-349.
3	SHEN X F, LIAO Y, DAI X, et al. BEM-based EKF-RTSS channel estimation for non-stationary doubly-selective channel[C]// Proc. of the IEEE/CIC International Conference on Communications in China, 2018: 536-541.
4	卢娜, 高丽, 沈轩帆. 基于无迹卡尔曼滤波的双选信道估计方法[J]. 山东大学学报(工学版), 2019, 49 (4): 130- 136.
	LU N , GAO L , SHEN X F . Double-selection channel estimation method based on unscented Kalman filter[J]. Journal of Shandong University (Engineering Edition), 2019, 49 (4): 130- 136.
5	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 physical layer wireless communication techniques: opportunities and challenges[J]. Journal on Communications, 2019, 40 (2): 19- 23.
6	WANG T Q , WEN C K , WANG H Q . Deep learning for wireless physical layer: opportunities and challenges[J]. China Communications, 2017, 14 (11): 92- 111. doi: 10.1109/CC.2017.8233654
7	YE H , LI G Y , JUANG J H . Power of deep learning for channel estimation and signal detection in OFDM systems[J]. IEEE Wireless Communications Letters, 2018, 7 (1): 114- 117. doi: 10.1109/LWC.2017.2757490
8	LI L J , CHEN H , CHANG H H . Deep residual learning meets OFDM channel estimation[J]. IEEE Wireless Communications Letters, 2020, 9 (5): 615- 618. doi: 10.1109/LWC.2019.2962796
9	YI X M , ZHONG C J . Deep learning for joint channel estimation and signal detection in OFDM systems[J]. IEEE Communications Letters, 2020, 24 (12): 2780- 2784. doi: 10.1109/LCOMM.2020.3014382
10	LIAO Y, HUA Y X, YAO H M, et al. ChanEstNet: a deep learning based channel estimation for high-speed scenarios[C]// Proc. of the IEEEInternational Conference on Communications, 2019.
11	LIAO Y , HUA Y X , CAI Y L . Deep learning based channel estimation algorithm for fast time-varying MIMO-OFDM systems[J]. IEEE Communications Letters, 2020, 24 (3): 572- 576. doi: 10.1109/LCOMM.2019.2960242
12	邵凯, 陈连成, 刘胤. 高移动性Jakes信道的学习与估计[J]. 系统工程与电子技术, 2021, 43 (4): 1119- 1125.
	SHAO K , CHEN L C , LIU Y . Learning and estimation of high mobility Jakes channel[J]. Systems Engineering and Electronics, 2021, 43 (4): 1119- 1125.
13	沈轩帆. 高速移动环境下的OFDM信道估计研究[D]. 重庆: 重庆大学, 2019.
	SHEN X F. Research on OFDM channel estimation in high-speed mobile environment[D]. Chongqing: Chongqing University, 2019.
14	廖勇, 陈颖. 基于基扩展模型的UKF-RTSS高可靠鲁棒V2V信道估计[J]. 电子与信息学报, 2022, 44 (5): 1792- 1799.
	LIAO Y , CHEN Y . Ultra-reliable and robust channel estimation using basis expansion model-Based UKF-RTSS scheme for V2V systems[J]. Journal of Electronics & Information Technology, 2022, 44 (5): 1792- 1799.
15	HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
16	HOCHREITER S , SCHMIDHUBER J . Long short-term memory[J]. Neural Computation, 1997, 9 (8): 1735- 1780. doi: 10.1162/neco.1997.9.8.1735
17	ZHENG Y R , XIAO C S , et al. Improved models for the generation of multiple uncorrelated Rayleigh fading waveforms[J]. IEEE Communications Letters, 2002, 6 (6): 256- 258.

参数	数值
载波频率/GHz	2.8
系统采样率/MHz	2.52
子载波数目	180
调制方式	QPSK
基向量维度Q	4
导频数量	3
循环前缀	18

算法	乘法运算次数
BEM_LS	8.0e6
TF_GRU	4.9e7
BEM_LMMSE	6.1e7
T-ChanEstNet	7.1e7
BEM_UKF	9.3e7
BEM_EKF_RTSS	1.2e8

[1]	Meng WANG, Bing ZHU. Application of uncertainty modeling in 2D and 3D object detection [J]. Systems Engineering and Electronics, 2023, 45(8): 2370-2376.
[2]	Kai SHAO, Ziqun DU, Guangyu WANG. CSI feedback method for dynamically adjusting compression rate based on model pruning [J]. Systems Engineering and Electronics, 2023, 45(8): 2615-2622.
[3]	Tianshu CUI, Dong WANG, Zhen HUANG. Automatic modulation classification based on lightweight network for space cognitive communication [J]. Systems Engineering and Electronics, 2023, 45(7): 2220-2226.
[4]	Yu JIANG, Qi YUAN, Zhitao HU, Weiwei WU, Xin GU. Airport arrival and departure delay time prediction based on meteorological factors [J]. Systems Engineering and Electronics, 2023, 45(6): 1722-1731.
[5]	Lihua YANG, Lulu REN, Bo HU, Yongqi SHAO, Qian NIE. Meta-learning based time-varying channel estimation method [J]. Systems Engineering and Electronics, 2023, 45(6): 1872-1879.
[6]	Ce JI, Boyan TIAN, Rong GENG, Boqun LI. FBMC/OQAM system channel estimation based on VSCBOMP algorithm [J]. Systems Engineering and Electronics, 2023, 45(4): 1193-1199.
[7]	Yang CHEN, Canhui LIAO, Kun ZHANG, Jian LIU, Pengju WANG. A signal modulation indentification algorithm based on self-supervised contrast learning [J]. Systems Engineering and Electronics, 2023, 45(4): 1200-1206.
[8]	Ye ZHANG, Yi HOU, Kewei OUYANG, Shilin ZHOU. Survey of univariate sequence data classification methods [J]. Systems Engineering and Electronics, 2023, 45(2): 313-335.
[9]	Zhengtu SHAO, Dengrong XU, Wenli XU, Hanzhong WANG. Radar active jamming recognition based on LSTM and residual network [J]. Systems Engineering and Electronics, 2023, 45(2): 416-423.
[10]	Renfei CHEN, Yong PENG, Zhongwen LI. A novel detector for floating objects based on continual unsupervised domain adaptation strategy [J]. Systems Engineering and Electronics, 2023, 45(11): 3391-3401.
[11]	Chensong TAO, Siwei CHEN, Shunping XIAO. SAR image interrupted sampling repeater jamming detection based on deep learning models [J]. Systems Engineering and Electronics, 2023, 45(11): 3465-3473.
[12]	Shiyang HE, Ling WANG, Daiyin ZHU, Jun QIAN. Thunderstorm prediction method based on spatiotemporal memory decoupling RNN [J]. Systems Engineering and Electronics, 2023, 45(11): 3474-3480.
[13]	Haoran LI, Wei XIONG, Yaqi CUI. An association method between SAR images and AIS information based on depth feature fusion [J]. Systems Engineering and Electronics, 2023, 45(11): 3491-3497.
[14]	Zichao LIU, Jiang WANG, Shaoming HE. Time and angle control guidance law based on deep learning [J]. Systems Engineering and Electronics, 2023, 45(11): 3579-3587.
[15]	Jianli DING, Qiqi ZHANG, Jing WANG, Weigang HUO. ADS-B anomaly detection method based on Transformer-VAE [J]. Systems Engineering and Electronics, 2023, 45(11): 3680-3689.

Deep learning based channel estimation for OFDM systems in fast time-varying channel

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 17

Related Articles 15

Recommended Articles

Metrics

Comments