首页 > 论文 > 激光与光电子学进展 > 57卷 > 1期(pp:10001--1)

基于机器学习的可见光通信信号处理研究现状 (封面文章)

Research Status of Machine Learning Based Signal Processing in Visible Light Communication (Cover Paper)

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

随着无线通信领域的发展,具有诸多优点的可见光通信(VLC)已经发展成为了一种具有广阔前景的通信手段。然而,可见光通信中的各种非线性效应会给其信号处理带来诸多的困难,并恶化系统的性能。机器学习在解决非线性问题方面具有很大的优势和潜力,结合机器学习算法的可见光通信技术必然具有巨大的研究价值。已有研究表明,传统的机器学习算法如K-means、DBSCAN以及支持向量机(SVM)等在预均衡、后均衡、抗系统抖动,以及相位纠正等方面均有很好的表现。而深度神经网络(DNN)则因为其强大的非线性拟合能力能够更进一步提升VLC系统的性能。对以上几种方法进行了分析和介绍,并对其在可见光通信信号处理领域的应用进行了分析与总结,希望可以为机器学习解决可见光通信方面的各种非线性问题提供参考。

Abstract

With the development of wireless communication, visible light communication (VLC) has become very promising technology owing to its many advantages. However, the nonlinear effect of VLC introduces many challenges for signal processing and deteriorates system performance. As machine learning has many advantages and significant potential for solving nonlinearity issues, the VLC that utilizes machine learning algorithms is bound to have tremendous research value. Existing research shows that traditional machine learning algorithms, such as K-means, DBSCAN, and support vector machine, perform well in pre-equalization, post-equalization, anti-system jitter, and phase correction. A deep neural network can further improve the performance of the VLC system because of its strong nonlinear fitting ability. In this article, we analyze the aforementioned methods and introduce their application to the signal processing in VLC. We hope this paper provides a reference for solving the nonlinearity problems related to machine learning in VLC.

广告组1 - 空间光调制器+DMD
补充资料

中图分类号:TN929.1

DOI:10.3788/LOP57.010001

所属栏目:综述

基金项目:科技部重点研发计划,国家自然科学基金面上项目;

收稿日期:2019-03-06

修改稿日期:2019-06-06

网络出版日期:2037-01-01

作者单位    点击查看

邹鹏:复旦大学通信科学与工程系电磁波信息科学教育部重点实验室, 上海 200433
赵一衡:复旦大学通信科学与工程系电磁波信息科学教育部重点实验室, 上海 200433
胡昉辰:复旦大学通信科学与工程系电磁波信息科学教育部重点实验室, 上海 200433
迟楠:复旦大学通信科学与工程系电磁波信息科学教育部重点实验室, 上海 200433

联系人作者:迟楠(nanchi@fudan.edu.cn)

备注:科技部重点研发计划,国家自然科学基金面上项目;

【1】Chi N, Haas H, Kavehrad M, et al. Visible light communications: demand factors, benefits and opportunities[Guest Editorial] [J]. IEEE Wireless Communications. 2015, 22(2): 5-7.

【2】Tanaka Y, Haruyama S, Nakagawa M. Wireless optical transmissions with white colored LED for wireless home links . [C]∥11th IEEE International Symposium on Personal Indoor and Mobile Radio Communications. PIMRC 2000. Proceedings (Cat. No.00TH8525), September 18-21, 2000, London, UK. New York: IEEE. 2000, 1325-1329.

【3】Haas H, Yin L, Wang Y L, et al. What is LiFi? [J]. Journal of Lightwave Technology. 2016, 34(6): 1533-1544.

【4】O''''Brien D. Minh H L, Zeng L B, et al. Indoor visible light communications: challenges and prospects [J]. Proceedings of SPIE. 2008, 7091: 709106.

【5】Jia K J, Jin B, Hao L. Performance analysis of optical OFDM adaptive bit-power loading in indoor visible light communications [J]. Laser & Optoelectronics Progress. 2019, 56(3): 030603.
贾科军, 靳斌, 郝莉. 室内可见光通信OFDM自适应比特功率加载算法性能分析 [J]. 激光与光电子学进展. 2019, 56(3): 030603.

【6】Yang Y F, Jiang M Z, Zhang Y, et al. Design of full duplex visible light communication system based on single light source [J]. Laser & Optoelectronics Progress. 2019, 56(1): 010603.
杨玉峰, 蒋明争, 张颖, 等. 基于单光源的全双工可见光通信系统设计 [J]. 激光与光电子学进展. 2019, 56(1): 010603.

【7】Neokosmidis I, Kamalakis T, Walewski J W, et al. Impact of nonlinear LED transfer function on discrete multitone modulation: analytical approach [J]. Journal of Lightwave Technology. 2009, 27(22): 4970-4978.

【8】Ying K, Yu Z H, Baxley R J, et al. Nonlinear distortion mitigation in visible light communications [J]. IEEE Wireless Communications. 2015, 22(2): 36-45.

【9】Inan B. Jeffrey Lee S C, Randel S, et al. Impact of LED nonlinearity on discrete multitone modulation [J]. Journal of Optical Communications and Networking. 2009, 1(5): 439-451.

【10】Wang Y G, Tao L, Huang X X, et al. 8-Gb/s RGBY LED-based WDM VLC system employing high-order CAP modulation and hybrid post equalizer [J]. IEEE Photonics Journal. 2015, 7(6): 7904507.

【11】Bishop C M. Pattern recognition and machine learning [M]. New York: Springer. 2006, 103-107.

【12】Zhuo L, Chen X Q, Xie Z P, et al. Simulation learning method for discovery of camouflage targets based on deep neural networks [J]. Laser & Optoelectronics Progress. 2019, 56(7): 071102.
卓刘, 陈晓琪, 谢振平, 等. 基于深度神经网络的迷彩目标发现仿真学习方法 [J]. 激光与光电子学进展. 2019, 56(7): 071102.

【13】Qu L, Wang K R, Chen L L, et al. Fast road detection based on RGBD images and convolutional neural network [J]. Acta Optica Sinica. 2017, 37(10): 101003.
曲磊, 王康如, 陈利利, 等. 基于RGBD图像和卷积神经网络的快速道路检测 [J]. 光学学报. 2017, 37(10): 101003.

【14】Zhu Y, Qin Y, Dong L, et al. Application cases of artificial intelligence in mobile communication networks: machine-learning-based channel estimator and signal detector [J]. Information and Communications Technologies. 2019, 13(1): 19-25.
朱玥, 覃尧, 董岚, 等. 人工智能在移动通信网络中的应用: 基于机器学习理论的信道估计与信号检测算法 [J]. 信息通信技术. 2019, 13(1): 19-25.

【15】Khan F N, Lu C. Lau A P T. Machine learning methods for optical communication systems . [C]∥Advanced Photonics 2017 (IPR, NOMA, Sensors, Networks, SPPCom, PS), July 24-27, 2017, New Orleans, Louisiana, United States. Washington, D.C.: OSA. 2017, SpW2F: 3.

【16】Khan F N. Shen T S R, Zhou Y D, et al. Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes [J]. IEEE Photonics Technology Letters. 2012, 24(12): 982-984.

【17】Tanimura T, Hoshida T, Rasmussen J C, et al. OSNR monitoring by deep neural networks trained with asynchronously sampled data . [C]∥2016 21st OptoElectronics and Communications Conference (OECC) held jointly with 2016 International Conference on Photonics in Switching (PS), July 3-7, 2016, Niigata, Japan. New York: IEEE. 2016, 16424746.

【18】Skoog R A, Banwell T C, Gannett J W, et al. Automatic identification of impairments using support vector machine pattern classification on eye diagrams [J]. IEEE Photonics Technology Letters. 2006, 18(22): 2398-2400.

【19】Tan M C, Khan F N. Al-Arashi W H, et al. Simultaneous optical performance monitoring and modulation format/bit-rate identification using principal component analysis [J]. Journal of Optical Communications and Networking. 2014, 6(5): 441-448.

【20】Gonzalez N G, Zibar D, Monroy I T. Cognitive digital receiver for burst mode phase modulated radio over fiber links . [C]∥36th European Conference and Exhibition on Optical Communication, September 19-23, 2010, Torino, Italy. New York: IEEE. 2010, 11636818.

【21】Khan F N, Zhou Y D. Lau A P T, et al. Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks [J]. Optics Express. 2012, 20(11): 12422-12431.

【22】Khan F N, Yu Y, Tan M C, et al. Experimental demonstration of joint OSNR monitoring and modulation format identification using asynchronous single channel sampling [J]. Optics Express. 2015, 23(23): 30337-30346.

【23】Khan F N, Zhong K P. Al-Arashi W H, et al. Modulation format identification in coherent receivers using deep machine learning [J]. IEEE Photonics Technology Letters. 2016, 28(17): 1886-1889.

【24】He H T, Wen C K, Jin S, et al. Deep learning-based channel estimation for beamspace mmWave massive MIMO systems [J]. IEEE Wireless Communications Letters. 2018, 7(5): 852-855.

【25】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. 2018, 7(1): 114-117.

【26】Ha Y, Niu W Q, Chi N. Frequency reshaping and compensation scheme based on deep neural network for a FTN CAP 9QAM signal in visible light communication system [J]. Proceedings of SPIE. 2019, 11048: 110482F.

【27】Chi N, Zhao Y H, Shi M, et al. Gaussian kernel-aided deep neural network equalizer utilized in underwater PAM8 visible light communication system [J]. Optics Express. 2018, 26(20): 26700-26712.

【28】Lu X Y, Lu C, Yu W X, et al. Memory-controlled deep LSTM neural network post-equalizer used in high-speed PAM VLC system [J]. Optics Express. 2019, 27(5): 7822-7833.

【29】Yu W X, Lu X Y, Chi N. Signal decision employing density-based spatial clustering of machine learning in PAM-4 VLC system [J]. Proceedings of SPIE. 2018, 10849: 108491D.

【30】Lu X Y, Qiao L, Zhou Y J, et al. An I-Q-Time 3-dimensional post-equalization algorithm based on DBSCAN of machine learning in CAP VLC system [J]. Optics Communications. 2019, 430: 299-303.

【31】Lu X Y, Wang K H, Qiao L, et al. Nonlinear compensation of multi-CAP VLC system employing clustering algorithm based perception decision [J]. IEEE Photonics Journal. 2017, 9(5): 7906509.

【32】Lu X Y, Zhao M M, Qiao L, et al. Non-linear compensation of multi-CAP VLC system employing pre-distortion base on clustering of machine learning . [C]∥Optical Fiber Communication Conference, March 11-15, 2018, San Diego, California, United States. Washington, D.C.: OSA. 2018, M2K: 1.

【33】Lu X, Zhou Y, Qiao L, et al. Amplitude jitter compensation of PAM-8 VLC system employing time-amplitude two-dimensional re-estimation base on density clustering of machine learning [J]. Physica Scripta. 2019, 94(5): 055506.

【34】Niu W Q, Ha Y, Chi N. Novel phase estimation scheme based on support vector machine for multiband-CAP visible light communication system . [C]∥2018 Asia Communications and Photonics Conference (ACP), October 26-29, 2018, Hangzhou, China. New York: IEEE. 2018, 18382490.

【35】Suykens J A K, Vandewalle J. Least squares support vector machine classifiers [J]. Neural Processing Letters. 1999, 9(3): 293-300.

【36】Scholkopf B, Smola A J. Learning with kernels: support vector machines, regularization, optimization, and beyond[M]. Cambridge: , 2001, 133-145.

【37】Haigh P A, Ghassemlooy Z, Rajbhandari S, et al. Visible light communications: 170 Mb/s using an artificial neural network equalizer in a low bandwidth white light configuration [J]. Journal of Lightwave Technology. 2014, 32(9): 1807-1813.

【38】Guan W P, Wu Y X, Xie C Y, et al. High-precision approach to localization scheme of visible light communication based on artificial neural networks and modified genetic algorithms [J]. Optical Engineering. 2017, 56(10): 106103.

【39】Haigh P A, Ghassemlooy Z, Papakonstantinou I, et al. A MIMO-ANN system for increasing data rates in organic visible light communications systems . [C]∥2013 IEEE International Conference on Communications (ICC), June 9-13, 2013, Budapest, Hungary. New York: IEEE. 2013, 5322-5327.

引用该论文

Zou Peng,Zhao Yiheng,Hu Fangchen,Chi Nan. Research Status of Machine Learning Based Signal Processing in Visible Light Communication[J]. Laser & Optoelectronics Progress, 2020, 57(1): 010001

邹鹏,赵一衡,胡昉辰,迟楠. 基于机器学习的可见光通信信号处理研究现状[J]. 激光与光电子学进展, 2020, 57(1): 010001

被引情况

【1】张慧颖,于海越,陈玲玲. 基于反向学习策略的自适应花授粉接收信号强度指示室内可见光定位. 中国激光, 2021, 48(1): 106001--1

【2】牟迪,蒙文,赵尚弘,王翔,刘文亚. 基于Wasserstein生成对抗网络的智能光通信. 中国激光, 2020, 47(11): 1106005--1

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF