• Overview of Chinese core journals
  • Chinese Science Citation Database(CSCD)
  • Chinese Scientific and Technological Paper and Citation Database (CSTPCD)
  • China National Knowledge Infrastructure(CNKI)
  • Chinese Science Abstracts Database(CSAD)
  • JST China
  • SCOPUS
CHEN Jiarun, YU Baoxian, WANG Jianying, ZHANG Han. A DNN-Based Channel Estimation Method for Spectral Efficient Frequency Division Multiplexing Systems[J]. Journal of South China Normal University (Natural Science Edition), 2020, 52(3): 17-21. DOI: 10.6054/j.jscnun.2020038
Citation: CHEN Jiarun, YU Baoxian, WANG Jianying, ZHANG Han. A DNN-Based Channel Estimation Method for Spectral Efficient Frequency Division Multiplexing Systems[J]. Journal of South China Normal University (Natural Science Edition), 2020, 52(3): 17-21. DOI: 10.6054/j.jscnun.2020038

A DNN-Based Channel Estimation Method for Spectral Efficient Frequency Division Multiplexing Systems

More Information
  • Received Date: September 20, 2019
  • Available Online: March 21, 2021
  • A channel estimation method based on deep neural network (DNN) for spectral efficient frequency division multiplexing (SEFDM) systems is proposed. The method employs uniform spaced orthogonal pilot symbols to achieve the channel estimation. To be specific, the received pilot signals are used as the input of the four-layer DNN in order to extract the channel features. Simulation results show that the proposed scheme can yield a smaller mean square error (MSE) and, in turn, perform better demodulation in comparison with the conventional least square (LS) method. In particular, the DNN-based method is more robust to the number of pilots, which indicates its superiority.
  • [1]
    YU B X, GUO C J, YI L Y, et al. 150-Gb/s SEFDM IM/DD transmission using log-MAP Viterbi decoding for short reach optical links[J]. Optics Express, 2018, 26(24):31075-31084. doi: 10.1364/OE.26.031075
    [2]
    张涵, 郭昌霖, 李家明, 等.基于混合导频辅助的大规模MI-MO上行链路信道估计[J].华南师范大学学报(自然科学版), 2016, 48(6):57-62. http://journal-n.scnu.edu.cn/article/id/3882

    ZHANG H, GUO C L, LI J M, et al. Hybrid pilot-aided channel estimation in massive MIMO uplink[J]. Journal of South China Normal University (Natural Science Edition), 2016, 48(6):57-62. http://journal-n.scnu.edu.cn/article/id/3882
    [3]
    RODRIGUES M, DARWAZWH I. A spectrally efficient frequency division multiplexing based communications system[C]//2003 Proceedings of the international OFDM workshop. Hamburg: IEEE, 2003: 48-49.
    [4]
    NOPCHINDA D, XU T, MAHER R, et al. Dual polarization coherent optical spectrally efficient frequency division multiplexing[J]. Photonics Technology Letters, 2016, 28(1):83-86. doi: 10.1109/LPT.2015.2485669
    [5]
    YU B X, ZHANG H, HONG X D, et al. Channel equalisation and data detection for SEFDM over frequency selective fading channels[J]. IET Communications, 2018, 12:2315-2323. doi: 10.1049/iet-com.2018.5114
    [6]
    CHORTI A, KANARAS I, RODRIGUES M R, et al. Joint channel equalization and detection of spectrally efficient FDM signals[C]//2010 IEEE 21st International Symposium on Personal Indoor and Mobile Radio Commiunications(PIMRC). Blacksburg: IEEE, 2010: 177-182.
    [7]
    ISAM S, DARWAZEH I. Robust channel estimation for spectrally efficient FDM system[C]//2012 19th International Conference on Telecommunication(ICT). New York: IEEE, 2010: 177-182.
    [8]
    YU T, ZHAO M, ZHONG J, et al. Low-complexity graph-based turbo equalization for single-carrier FTN signaling[J]. IET Signal Processing, 2017, 11(7):838-845. doi: 10.1049/iet-spr.2016.0251
    [9]
    GHANNAM H, DARWAZEH I. Robust channel estimation methods for spectrally efficient FDM systems[C]//2018 IEEE 87th Vehicular Technology Conference(VTC Spring). New York: IEEE, 2018: 1-6.
    [10]
    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, 2017, 7(1):114-117. http://cn.bing.com/academic/profile?id=c5b0bb6adf4ec51f581a49298f347364&encoded=0&v=paper_preview&mkt=zh-cn
    [11]
    WANG X, GAO L, MAO S, et al. CSI-based fingerprinting for indoor localization:a deep learning approach[J]. IEEE Transactions on Vehicular Technology, 2017, 66(1):763-776. http://cn.bing.com/academic/profile?id=e1379d28a7ae970be05f46d29233fd93&encoded=0&v=paper_preview&mkt=zh-cn
    [12]
    CHEN S, GIBSON G J, COWN C F N, et al. Adaptive equalization of finite non-linear channels using multilayer perceptrons[J]. Signal Processing, 1990, 20(2):107-119. https://www.sciencedirect.com/science/article/pii/016516849090122F
    [13]
    NACHMANI E, BEERY Y, BURSHTEIN D. Learning to decode linear codes using deep learning[C]//54th Annual Allerton Conference on Communication, Control Computing. New York: IEEE, 2016: 341-346.
    [14]
    MAZO J E. Faster-than-nyquist signaling[J]. Bell System Technical Journal, 1975, 54(8):1451-1462. doi: 10.1002/j.1538-7305.1975.tb02043.x
    [15]
    YU B X, ZHANG H, DAI X H. A low-complexity demodulation technique for spectrally efficient FDM systems using decision-feedback[J]. IET Communications, 2017, 11:2386-2392. doi: 10.1049/iet-com.2017.0069
    [16]
    SCHMIDHUBER J. Deep learning in neural networks:an overview[J]. Neural Networks, 2015, 61:85-117. doi: 10.1016/j.neunet.2014.09.003
    [17]
    GLOROT X, BENGIO Y. Understanding the difficulty of training deep feedforward neural networks[C]//International Conference on Artificial Intelligence and Statistics. Sardinia: Journal of Machine Learning Research, 2010: 249-256.
  • Cited by

    Periodical cited type(4)

    1. 韦昀昊. 高铁环境下基于循环神经网络的无线传播信道模型研究. 微型电脑应用. 2024(03): 176-179 .
    2. 郝立元. 无人机中继通信轨迹和功率优化策略研究. 电子制作. 2021(01): 89-90+100 .
    3. 陈慧敏. 融合深度学习和强化学习的5G无线资源管理. 移动通信. 2021(04): 135-139+148 .
    4. 方世林,赵子琪,余丹,谢文武,方世峰. 基于深度学习的NOMA系统符号检测算法研究. 湖南理工学院学报(自然科学版). 2021(04): 32-36 .

    Other cited types(3)

Catalog

    Article views (1070) PDF downloads (93) Cited by(7)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return