Independent component analysis for preprocessing optical signals in support of multi-user communication

被引:6
作者
Aveta, Federica [1 ]
Refai, Hazem H. [1 ]
LoPresti, Peter [2 ]
Tedder, Sarah A. [3 ]
Schoenholz, Bryan L. [3 ]
机构
[1] Univ Oklahoma, Dept Elect Engn, Tulsa, OK 74135 USA
[2] Univ Tulsa, Dept Elect Engn, Tulsa, OK 74104 USA
[3] NASA, Glenn Res Ctr, Cleveland, OH USA
来源
FREE-SPACE LASER COMMUNICATION AND ATMOSPHERIC PROPAGATION XXX | 2018年 / 10524卷
关键词
BSS; ICA; FSO; FastICA; ALGORITHMS;
D O I
10.1117/12.2290941
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Free Space Optical (FSO) communication is widely recognized for its powerful features, especially when compared to other wireless technologies utilized in point-to-point communication links. Although current literature focuses primarily on point-to-point transmission, multi-user FSO systems are beginning to draw significant attention. The primary objective in a multi-user communication system is to estimate individually transmitted signals from received signals, namely Blind Source Separation (BSS). A solution to the BSS problem in an FSO multi-user communication link is proposed. A multi-point FSO system composed of two independent transmitters operating at different wavelengths and a dual path fiber bundle receiver was used. The FastICA algorithm was exploited for multi-user detection. Experimental results demonstrate that this method can separate original transmitted signals from their received mixtures. Effects of signal power, data rate, misalignment error, and turbulence severity on signal separation are also explored to define the working range for achieving best performance.
引用
收藏
页数:9
相关论文
共 16 条
[1]  
[Anonymous], 2017, P IEEE INT C COMM IC
[2]  
Aveta F, 2017, 2017 COGN COMM AER A, P1, DOI DOI 10.1109/CCAAW.2017.8001890
[3]  
Comon P, 2010, HANDBOOK OF BLIND SOURCE SEPARATION: INDEPENDENT COMPONENT ANALYSIS AND APPLICATIONS, P1
[4]   Approximating the Kullback Leibler Divergence between Gaussian Mixture Models [J].
Hershey, John R. ;
Olsen, Peder A. .
2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL IV, PTS 1-3, 2007, :317-320
[5]   Fast and robust fixed-point algorithms for independent component analysis [J].
Hyvärinen, A .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1999, 10 (03) :626-634
[6]   Independent component analysis:: algorithms and applications [J].
Hyvärinen, A ;
Oja, E .
NEURAL NETWORKS, 2000, 13 (4-5) :411-430
[7]  
Kasturiwale H., 2014, INT J ENG RES TECHNO, V3, P674, DOI [https://doi.org/10.1145/2007052.2007079, DOI 10.1145/2007052.2007079]
[8]   Optical Communication in Space: Challenges and Mitigation Techniques [J].
Kaushal, Hemani ;
Kaddoum, Georges .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2017, 19 (01) :57-96
[9]   Survey on Free Space Optical Communication: A Communication Theory Perspective [J].
Khalighi, Mohammad Ali ;
Uysal, Murat .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2014, 16 (04) :2231-2258
[10]   Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources [J].
Lee, TW ;
Girolami, M ;
Sejnowski, TJ .
NEURAL COMPUTATION, 1999, 11 (02) :417-441