| Citation: | LIU Chunhui, WANG Meilin, DONG Zanliang, et al. Channel estimation of air-ground data link based on modulated convolutional neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(3): 533-543. doi: 10.13700/j.bh.1001-5965.2020.0591(in Chinese)
|
Aimed at the inaccuracy of channel estimation of orthogonal frequency division multiplexing (OFDM) system in the complex air-ground data link environment, this paper proposes a channel estimation algorithm based on the modulated convolutional neural network (MCNN) and bidirectional long short-term memory (BiLSTM) network. First, least square (LS) algorithm is used to extract the initial channel state information (CSI), then MCNN network is used to extract the depth characteristics of the initial CSI while compressing the network model, and finally BiLSTM network is used to predict the final CSI and realize channel estimation. In the aspect of experimental verification, the air-ground channel model constructed is used to generate the channel coefficient dataset, so as to realize the training and testing of neural network model. The simulation results show that compared with the traditional methods and the existing deep learning method, the proposed channel estimation method has a lower estimation error, and the performance of the bit error ratio (BER) of the system under the condition of high SNR is improved by nearly an order of magnitude. Due to the introduction of the modulation filter technology, the number of network model parameters decreases remarkably with the increase of the number of neural network layers.
| [1] |
丛伟, 李宏. 基于OFDM的无人机通信链路[C]//2006中国无人机大会, 2006: 629-634.
CONG W, LI H. Communication link of UAV based on OFDM[C]//2006 China UAV Conference, 2006: 629-634(in Chinese).
|
| [2] |
章勇. OFDM系统信道估计技术研究[D]. 北京: 北京邮电大学, 2008: 10-12.
ZHANG Y. Research on channel estimation techniques of OFDM system[D]. Beijing: Beijing University of Posts and Telecommunications, 2008: 10-12(in Chinese).
|
| [3] |
董秀洁, 王莉, 王素菊. 一种改进的LS信道估计算法[J]. 哈尔滨理工大学学报, 2009, 14(1): 47-50. doi: 10.3969/j.issn.1007-2683.2009.01.012
DONG X J, WANG L, WANG S J. An improved LS channel estimation algorithm[J]. Journal of Harbin University of Science and Technology, 2009, 14(1): 47-50(in Chinese). doi: 10.3969/j.issn.1007-2683.2009.01.012
|
| [4] |
WANG J, WEN O Y, LI S Q. Soft-output MMSE MIMO detector under ML channel estimation and channel correlation[J]. IEEE Signal Processing Letters, 2009, 16(8): 667-670. doi: 10.1109/LSP.2009.2021368
|
| [5] |
ZHANG K, XUE L S, LIU X P, et al. Performance comparison of LS, LMMSE channel estimation method in frequency and time domain for OQAM/OFDM systems[C]//2017 IEEE 17th International Conference on Communication Technology (ICCT). Piscataway: IEEE Press, 2017: 224-228.
|
| [6] |
YE H, LI G Y, JUANG B 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
|
| [7] |
HE H T, WEN C K, JIN S, et al. A model-driven deep learning network for MIMO detection[C]//2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). Piscataway: IEEE Press, 2018: 584-588.
|
| [8] |
GAO X X, JIN S, WEN C K, et al. ComNet: Combination of deep learning and expert knowledge in OFDM receivers[J]. IEEE Communications Letters, 2018, 22(12): 2627-2630. doi: 10.1109/LCOMM.2018.2877965
|
| [9] |
廖勇, 花远肖, 姚海梅. 基于深度学习的OFDM信道估计[J]. 重庆邮电大学学报(自然科学版), 2019, 31(3): 348-353. https://www.cnki.com.cn/Article/CJFDTOTAL-CASH201903009.htm
LIAO Y, HUA Y X, YAO H M. Channel estimation based on deep learning for OFDM systems[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2019, 31(3): 348-353(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-CASH201903009.htm
|
| [10] |
廖勇, 花远肖, 姚海梅, 等. 高速移动环境下基于深度学习的信道估计方法[J]. 电子学报, 2019, 47(8): 1701-1707. doi: 10.3969/j.issn.0372-2112.2019.08.013
LIAO Y, HUA Y X, YAO H M, et al. Channel estimation method based on deep learning in high-speed mobile environments[J]. Acta Electronica Sinica, 2019, 47(8): 1701-1707(in Chinese). doi: 10.3969/j.issn.0372-2112.2019.08.013
|
| [11] |
SAINATH T N, VINYALS O, SENIOR A, et al. Convolutional, long short-term memory, fully connected deep neural networks[C]//2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway: IEEE Press, 2015: 4580-4584.
|
| [12] |
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
|
| [13] |
刘步花, 丁丹, 杨柳. 基于神经网络的OFDM信道补偿与信号检测[J]. 北京航空航天大学学报, 2020, 46(7): 1363-1370. doi: 10.13700/j.bh.1001-5965.2019.0456
LIU B H, DING D, YANG L. Channel compensation and signal detection of OFDM based on neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7): 1363-1370(in Chinese). doi: 10.13700/j.bh.1001-5965.2019.0456
|
| [14] |
ZHANG J, WEN C K, JIN S, et al. Artificial intelligence-aided receiver for a CP-free OFDM system: Design, simulation, and experimental test[J]. IEEE Access, 2019, 7: 58901-58914. doi: 10.1109/ACCESS.2019.2914928
|
| [15] |
MATOLAK D W, SUN R Y. Air-ground channel characterization for unmanned aircraft systems—Part Ⅰ: Methods, measurements, and models for over-water settings[J]. IEEE Transactions on Vehicular Technology, 2017, 66(1): 26-44. doi: 10.1109/TVT.2016.2530306
|
| [16] |
MATOLAK D W, SUN R Y. Air-ground channel characterization for unmanned aircraft systems—Part Ⅱ: Hilly and mountainous settings[J]. IEEE Transactions on Vehicular Technology, 2017, 66(3): 1913-1925. doi: 10.1109/TVT.2016.2585504
|
| [17] |
MATOLAK D W, SUN R Y. Air-ground channel characterization for unmanned aircraft systems—Part Ⅱ: The suburban and nearurban environments[J]. IEEE Transactions on Vehicular Technology, 2017, 66(8): 6607-6618. doi: 10.1109/TVT.2017.2659651
|
| [18] |
HAN S, POOL J, TRAN J, et al. Learning both weights and connections for efficient neural networks[EB/OL]. (2015-10-30)[2020-10-15]. https://arxiv.org/abs/1506.02626v3.
|
| [19] |
HAN S, MAO H Z, DALLY W J. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding[EB/OL]. (2016-02-15)[2020-10-15]. https://arxiv.org/abs/1510.00149.
|
| [20] |
SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2016: 2818-2826.
|
| [21] |
WANG X D, ZHANG B C, LI C, et al. Modulated convolutional networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 840-848.
|
| [22] |
SCHUSTER M, PALIWAL K K. Bidirectional recurrent neural networks[J]. IEEE Transactions on Signal Processing, 1997, 45(11): 2673-2681. doi: 10.1109/78.650093
|
| [23] |
姚宜东, 徐毅, 杨新华, 等. 介质表面电磁波反射和透射能量的分析与计算[J]. 通信技术, 2016, 49(5): 558-562. doi: 10.3969/j.issn.1002-0802.2016.05.010
YAO Y D, XU Y, YANG X H, et al. Analysis and calculation on reflection and transmission energy of electromagnetic wave at dielectric interface[J]. Communications Technology, 2016, 49(5): 558-562(in Chinese). doi: 10.3969/j.issn.1002-0802.2016.05.010
|
| [24] |
WEN C K, SHIH W T, JIN S. Deep learning for massive MIMO CSI feedback[J]. IEEE Wireless Communications Letters, 2018, 7(5): 748-751. doi: 10.1109/LWC.2018.2818160
|
| [25] |
ZHANG D N, DING W R, LIU C H, et al. Modulated autocorrelation convolution networks for automatic modulation classification based on small sample set[J]. IEEE Access, 2020, 8: 27097-27105. doi: 10.1109/ACCESS.2020.2971586
|
| [26] |
DAO D N, TELLAMBURA C. Intercarrier interference self-cancellation space-frequency codes for MIMO-OFDM[J]. IEEE Transactions on Vehicular Technology, 2005, 54(5): 1729-1738. doi: 10.1109/TVT.2005.853477
|
Figures(12) / Tables(2)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
