Volume 39 Issue 9
Sep.  2020
Turn off MathJax
Article Contents
Yanan Tian, Xiao Han, Jingwei Yin, Hongxia Chen, Qingyu Liu. An improved least mean square/fourth direct adaptive equalizer for under-water acoustic communications in the Arctic[J]. Acta Oceanologica Sinica, 2020, 39(9): 133-139. doi: 10.1007/s13131-020-1653-6
Citation: Yanan Tian, Xiao Han, Jingwei Yin, Hongxia Chen, Qingyu Liu. An improved least mean square/fourth direct adaptive equalizer for under-water acoustic communications in the Arctic[J]. Acta Oceanologica Sinica, 2020, 39(9): 133-139. doi: 10.1007/s13131-020-1653-6

An improved least mean square/fourth direct adaptive equalizer for under-water acoustic communications in the Arctic

doi: 10.1007/s13131-020-1653-6
Funds:  The National Natural Science Foundation of China under contract Nos 61631008 and 61901136; the National Key Research and Development Program of China under contract No. 2018YFC1405904; the Fok Ying-Tong Education Foundation under contract No. 151007; the Heilongjiang Province Outstanding Youth Science Fund under contract No. JC2017017; the Opening Funding of Science and Technology on Sonar Laboratory under contract No. 6142109KF201802; the Innovation Special Zone of National Defense Science and Technology.
More Information
  • Corresponding author: E-mail: hanxiao1322@hrbeu.edu.cn
  • Received Date: 2019-10-13
  • Accepted Date: 2019-11-13
  • Available Online: 2020-12-28
  • Publish Date: 2020-09-25
  • An improved least mean square/fourth direct adaptive equalizer (LMS/F-DAE) is proposed in this paper for underwater acoustic communication in the Arctic. It is able to process complex-valued baseband signals and has better equalization performance than LMS. Considering the sparsity feature of equalizer tap coefficients, an adaptive norm (AN) is incorporated into the cost function which is utilized as a sparse regularization. The norm constraint changes adaptively according to the amplitude of each coefficient. For small-scale coefficients, the sparse constraint exists to accelerate the convergence speed. For large-scale coefficients, it disappears to ensure smaller equalization error. The performance of the proposed AN-LMS/F-DAE is verified by the experimental data from the 9th Chinese National Arctic Research Expedition. The results show that compared with the standard LMS/F-DAE, AN-LMS/F-DAE can promote the sparse level of the equalizer and achieve better performance.
  • loading
  • [1]
    Berger C R, Zhou Shengli, Preisig J C, et al. 2010. Sparse channel estimation for multicarrier underwater acoustic communication: from subspace methods to compressed sensing. IEEE Transactions on Signal Processing, 58(3): 1708–1721. doi: 10.1109/TSP.2009.2038424
    [2]
    Brandwood D H. 1983. A complex gradient operator and its application in adaptive array theory. IEE Proceedings H Microwaves, Optics and Antennas, 130(1): 11–16. doi: 10.1049/ip-h-1.1983.0004
    [3]
    Chen Yilun, Gu Yuantao, Hero A O. 2009. Sparse LMS for system identification. In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. Taipei, Taiwan: IEEE, 3125–3128
    [4]
    Duan Weimin, Tao Jun, Zheng Y R. 2018. Efficient adaptive turbo equalization for multiple-input-multiple-output underwater acoustic communications. IEEE Journal of Oceanic Engineering, 43(3): 792–804. doi: 10.1109/JOE.2017.2707285
    [5]
    Eksioglu E M. 2014. Group sparse RLS algorithms. International Journal of Adaptive Control and Signal Processing, 28(12): 1398–1412. doi: 10.1002/acs.2449
    [6]
    Eksioglu E M, Tanc A K. 2011. RLS algorithm with convex regularization. IEEE Signal Processing Letters, 18(8): 470–473. doi: 10.1109/LSP.2011.2159373
    [7]
    Falconer D, Ariyavisitakul S L, Benyamin-Seeyar A, et al. 2002. Frequency domain equalization for single-carrier broadband wireless systems. IEEE Communications Magazine, 40(4): 58–66. doi: 10.1109/35.995852
    [8]
    Freitag L, Koski P, Morozov A, et al. 2012. Acoustic communications and navigation under Arctic ice. In: 2012 Oceans. Hampton Roads, VA, USA: IEEE, 1–8
    [9]
    Guan Gui, Mehbodniya A, Adachi F. 2013a. Least mean square/fourth algorithm for adaptive sparse channel estimation. In: 2013 IEEE 24th Annual IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications. London, UK: IEEE, 296–300
    [10]
    Guan Gui, Wei Peng, Adachi F. 2013b. Adaptive system identification using robust LMS/F algorithm. International Journal of Communication Systems, 27(11): 2956–2963
    [11]
    Lee Y, Cox D C. 1997. Adaptive DFE with regularization for indoor wireless data communications. In: IEEE Global Telecommunications Conference. Conference Record. Phoenix, AZ, USA: IEEE, 47–51
    [12]
    Li Haili, Ke Changqing, Zhu Qinghui, et al. 2019. Spatial-temporal variations in net primary productivity in the Arctic from 2003 to 2016. Acta Oceanologica Sinica, 38(8): 111–121. doi: 10.1007/s13131-018-1274-5
    [13]
    Li Weichang, Preisig J C. 2007. Estimation of rapidly time-varying sparse channels. IEEE Journal of Oceanic Engineering, 32(4): 927–939. doi: 10.1109/JOE.2007.906409
    [14]
    Liu Lu, Sun Dajun, Zhang Youwen. 2017. A family of sparse group lasso RLS algorithms with adaptive regularization parameters for adaptive decision feedback equalizer in the underwater acoustic communication system. Physical Communication, 23: 114–124. doi: 10.1016/j.phycom.2017.03.005
    [15]
    Mendel J M. 1991. Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications. Proceedings of the IEEE, 79(3): 278–305. doi: 10.1109/5.75086
    [16]
    Pelekanakis K, Chitre M. 2010. Comparison of sparse adaptive filters for underwater acoustic channel equalization/estimation. In: 2010 IEEE International Conference on Communication Systems. Singapore, Singapore: IEEE, 395–399
    [17]
    Pelekanakis K, Chitre M. 2013. New sparse adaptive algorithms based on the natural gradient and the l0 -norm. IEEE Journal of Oceanic Engineering, 38(2): 323–332. doi: 10.1109/JOE.2012.2221811
    [18]
    Stojanovic M, Catipovic J, Proakis J G. 1993. Adaptive multichannel combining and equalization for underwater acoustic communications. The Journal of the Acoustical Society of America, 94(3): 1621–1631. doi: 10.1121/1.408135
    [19]
    Tao Jun, An Liang, Zheng Y R. 2017. Enhanced adaptive equalization for MIMO underwater acoustic communications. OCEANS 2017-Anchorage. Anchorage, AK, USA: IEEE, 1–5
    [20]
    Vanbleu K, Ysebaert G, Cuypers G, et al. 2006. Adaptive bit rate maximizing time-domain equalizer design for DMT-based systems. IEEE Transactions on Signal Processing, 54(2): 483–498. doi: 10.1109/TSP.2005.861901
    [21]
    Vlachos E, Lalos A S, Berberidis K. 2012. Stochastic gradient pursuit for adaptive equalization of sparse multipath channels. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2(3): 413–423. doi: 10.1109/JETCAS.2012.2214631
    [22]
    Walach E, Widrow B. 1984. The least mean fourth (LMF) adaptive algorithm and its family. IEEE Transactions on Information Theory, 30(2): 275–283. doi: 10.1109/TIT.1984.1056886
    [23]
    Wang Yanshuo, Huang Fei, Fan Tingting. 2017. Spatio-temporal variations of Arctic amplification and their linkage with the Arctic oscillation. Acta Oceanologica Sinica, 36(8): 42–51. doi: 10.1007/s13131-017-1025-z
    [24]
    Wu Feiyun, Tong Feng. 2013. Non-uniform norm constraint LMS algorithm for sparse system identification. IEEE Communications Letters, 17(2): 385–388. doi: 10.1109/LCOMM.2013.011113.121586
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)

    Article Metrics

    Article views (165) PDF downloads(4) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return