Non-data-aided joint bit-rate and modulation format identification for next-generation heterogeneous optical networks

被引:39
作者
Khan, Faisal Nadeem [1 ]
Zhou, Yudi [2 ]
Sui, Qi [3 ]
Lau, Alan Pak Tao [3 ]
机构
[1] Univ Sains Malaysia, Sch Elect & Elect Engn, George Town, Malaysia
[2] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA
[3] Hong Kong Polytech Univ, Photon Res Ctr, Kowloon, Hong Kong, Peoples R China
关键词
Bit-rate and modulation format identification; Heterogeneous fiber-optic networks; Optical performance monitoring; Artificial neural networks; Fiber-optic communication;
D O I
10.1016/j.yofte.2013.12.001
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
A novel and cost-effective technique for simultaneous bit-rate and modulation format identification (BR-MFI) in next-generation heterogeneous optical networks is proposed. This technique utilizes an artificial neural network (ANN) in conjunction with asynchronous delay-tap plots (ADTPs) to enable low-cost joint BR-MFI at the receivers as well as at the intermediate network nodes without requiring any prior information from the transmitters. The results of numerical simulations demonstrate successful identification of several commonly-used bit-rates and modulation formats with estimation accuracies in excess of 99.7%. The effectiveness of proposed technique under different channel conditions i.e. optical signal-to-noise ratio (OSNR) in the range of 14-28 dB, chromatic dispersion (CD) in the range of -500 to 500 ps/nm and differential group delay (DGD) in the range of 0-10 ps, is investigated and it has been shown that the proposed technique is robust against all these impairments. (C) 2013 Elsevier Inc. All rights reserved.
引用
收藏
页码:68 / 74
页数:7
相关论文
共 21 条
  • [1] [Anonymous], OPT FIB COMM C AN CA
  • [2] [Anonymous], 1996, Automatic Modulation Recognition of Communication Signals
  • [3] Optical networking: Past, present, and future
    Berthold, Joseph
    Saleh, Adel A. M.
    Blair, Loudon
    Simmons, Jane M.
    [J]. JOURNAL OF LIGHTWAVE TECHNOLOGY, 2008, 26 (9-12) : 1104 - 1118
  • [4] Borkowski R., 2013, OPT FIB COMM C OFC A
  • [5] Chan CCK, 2010, OPTICAL PERFORMANCE MONITORING: ADVANCED TECHNIQUES FOR NEXT-GENERATION PHOTONIC NETWORKS, pXLIII, DOI 10.1016/B978-0-12-374950-5.00024-9
  • [6] Survey of automatic modulation classification techniques: classical approaches and new trends
    Dobre, O. A.
    Abdi, A.
    Bar-Ness, Y.
    Su, W.
    [J]. IET COMMUNICATIONS, 2007, 1 (02) : 137 - 156
  • [7] Dunne RobertA., 2007, A statistical approach to neural networks for pattern recognition
  • [8] Gonzalez N.G., 2010, 36 EUR C EXH OPT COM
  • [9] Application of amplitude histograms to monitor performance of optical channels
    Hanik, N
    Gladisch, A
    Caspar, C
    Strebel, B
    [J]. ELECTRONICS LETTERS, 1999, 35 (05) : 403 - 404
  • [10] Designing a neural network for forecasting financial and economic time series
    Kaastra, I
    Boyd, M
    [J]. NEUROCOMPUTING, 1996, 10 (03) : 215 - 236