A Reweighted Least Squares Approach to QAM Detector for Blind Equalization

被引:2
|
作者
Konishi, Katsumi [1 ]
Furukawa, Toshihiro [2 ]
机构
[1] Kogakuin Univ, Dept Comp Sci, Tokyo, Japan
[2] Tokyo Univ Sci, TheDepartment ofManagement Engn, Tokyo 162, Japan
关键词
Blind equalization; iteratively reweighted least squares; maximum likelihood detection; SEMIDEFINITE RELAXATION;
D O I
10.1109/LSP.2011.2118202
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This letter proposes a reweighted least squares algorithm for quadrature amplitude modulation (QAM) detector in blind equalization. Because the QAM detection problem is a non-convex combinatorial optimization problem, it is relaxed into a problem of minimizing the sum of logarithmic functions in order to overcome the combinatorial complexity. To find a local optimal solution of the problem, an iterative reweighted least squares based algorithm is proposed. Simulation results show that the proposed algorithm improves the accuracy of QAM detection in blind equalization.
引用
收藏
页码:259 / 262
页数:4
相关论文
共 50 条
  • [41] Smoothed Low Rank and Sparse Matrix Recovery by Iteratively Reweighted Least Squares Minimization
    Lu, Canyi
    Lin, Zhouchen
    Yan, Shuicheng
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2015, 24 (02) : 646 - 654
  • [42] A new cost function for the blind equalization of cross-QAM signals
    Abrar, S
    17TH ICM 2005: 2005 INTERNATIONAL CONFERENCE ON MICROELECTRONICS, PROCEEDINGS, 2005, : 290 - 295
  • [43] A New Multi-Modulus Blind Equalization Algorithm for QAM Signals
    Li, Chisheng
    Dong, Wenjuan
    Wan, Guojin
    Chen, Limin
    2009 5TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-8, 2009, : 2238 - 2241
  • [44] Iteratively Reweighted Least Squares Fiducial Interval for Variance in Unbalanced Variance Components Model
    Jiratampradab, Arisa
    Suntornchost, Jiraphan
    Supapakorn, Thidaporn
    MATHEMATICS, 2025, 13 (01)
  • [45] LOW-RANK MATRIX RECOVERY VIA ITERATIVELY REWEIGHTED LEAST SQUARES MINIMIZATION
    Fornasier, Massimo
    Rauhut, Holger
    Ward, Rachel
    SIAM JOURNAL ON OPTIMIZATION, 2011, 21 (04) : 1614 - 1640
  • [46] Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery
    Kuemmerle, Christian
    Sigl, Juliane
    JOURNAL OF MACHINE LEARNING RESEARCH, 2018, 19
  • [47] Performance Comparison of Supervised and Unsupervised/Blind Equalization Algorithms for QAM Transmitted Constellations
    Vanka, Ram Nishanth
    Murty, S. Balarama
    Mouli, B. Chandra
    2014 INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND INTEGRATED NETWORKS (SPIN), 2014, : 316 - 321
  • [48] Blind equalization algorithms for dual-mode CAP-QAM reception
    Garth, LM
    Yang, J
    Werner, JJ
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2001, 49 (03) : 455 - 466
  • [49] Performance analysis of a family of adaptive blind equalization algorithms for square-QAM
    Azim, Ali W.
    Abrar, Shafayat
    Zerguine, Azzedine
    Nandi, Asoke K.
    DIGITAL SIGNAL PROCESSING, 2016, 48 : 163 - 177
  • [50] A criterion for blind equalization and carrier-phase recovery of QAM based on kurtosis
    Wang, Da-Lei
    Yang, Bin
    Wu, Ying
    Wang, Xiu-Xiu
    Tien Tzu Hsueh Pao/Acta Electronica Sinica, 2014, 42 (07): : 1403 - 1409