L0-Norm Based Adaptive Equalization with PMSER Criterion for Underwater Acoustic Communications

被引:0
|
作者
Fang, Tian [1 ]
Liu, Feng [1 ]
LI, Conggai [1 ]
Chen, Fangjiong [2 ]
Xu, Yanli [1 ]
机构
[1] Shanghai Maritime Univ, Coll Informat Engn, Shanghai, Peoples R China
[2] South China Univ Technol, Sch Elect & Informat Engn, Guangzhou, Peoples R China
基金
中国国家自然科学基金; 上海市自然科学基金;
关键词
underwater acoustic channels; sparsity selection; PMSER; L0 norm approximation; adaptive equalization;
D O I
10.1587/transfun.2022EAL2069
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Underwater acoustic channels (UWA) are usually sparse, which can be exploited for adaptive equalization to improve the system performance. For the shallow UWA channels, based on the proportional minimum symbol error rate (PMSER) criterion, the adaptive equalization framework requires the sparsity selection. Since the sparsity of the L0 norm is stronger than that of the L1, we choose it to achieve better convergence. However, because the L0 norm leads to NP-hard problems, it is difficult to find an efficient solution. In order to solve this problem, we choose the Gaussian function to approximate the L0 norm. Simulation results show that the proposed scheme obtains better performance than the L1 based counterpart.
引用
收藏
页码:947 / 951
页数:5
相关论文
共 50 条
  • [21] Adaptive Turbo Equalization for Differential OFDM Systems in Underwater Acoustic Communications
    Zhao, Shiduo
    Yan, Shefeng
    Xi, Junyi
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (11) : 13937 - 13941
  • [22] Sparse Adaptive Filters Estimation/Equalization Comparison for Underwater Acoustic Communications
    Khan, M. T. A.
    Maqsood, B.
    Ishaq, Z.
    INFRARED, MILLIMETER-WAVE, AND TERAHERTZ TECHNOLOGIES V, 2018, 10826
  • [23] Sparse normalized subband adaptive filter algorithm with l0-norm constraint
    Yu, Yi
    Zhao, Haiquan
    Chen, Badong
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2016, 353 (18): : 5121 - 5136
  • [24] An Adaptive Sparse Array Beamforming Algorithm Based on Approximate L0-norm and Logarithmic Cost
    Wang, Haixu
    Li, YingSong
    APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL, 2021, 36 (07): : 838 - 843
  • [25] Homotopy Methods Based on l0-Norm for Compressed Sensing
    Dong, Zhengshan
    Zhu, Wenxing
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 29 (04) : 1132 - 1146
  • [26] l0-NORM FEATURE LMS ALGORITHMS
    Yazdanpanah, Hamed
    Apolinario, Jose A., Jr.
    Diniz, Paulo S. R.
    Lima, Markus V. S.
    2018 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP 2018), 2018, : 3111 - 315
  • [27] Compression and denoising using l0-norm
    Andy C. Yau
    Xuecheng Tai
    Michael K. Ng
    Computational Optimization and Applications, 2011, 50 : 425 - 444
  • [28] l0-Norm Variable Adaptive Selection for Geographically Weighted Regression Model
    Wu, Bo
    Yan, Jinbiao
    Cao, Kai
    ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS, 2023, 113 (05) : 1190 - 1206
  • [29] L0-norm constraint normalized logarithmic subband adaptive filter algorithm
    Shen, Zijie
    Huang, Tianmin
    Zhou, Kun
    SIGNAL IMAGE AND VIDEO PROCESSING, 2018, 12 (05) : 861 - 868
  • [30] Adaptive continuation based smooth l0-norm approximation for compressed sensing MR image reconstruction
    Datta, Sumit
    Paul, Joseph Suresh
    JOURNAL OF MEDICAL IMAGING, 2024, 11 (03)