Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks.
We propose a simple and cost-effective technique for modulation format identification (MFI) in next-generation heterogeneous fiber-optic networks using an artificial neural network (ANN) trained with the features extracted from the asynchronous amplitude histograms (AAHs). Results of numerical simulations conducted for six different widely-used modulation formats at various data rates demonstrate that the proposed technique can effectively classify all these modulation formats with an overall estimation accuracy of 99.6% and also in the presence of various link impairments. The proposed technique employs extremely simple hardware and digital signal processing (DSP) to enable MFI and can also be applied for the identification of other modulation formats at different data rates without necessitating hardware changes.
Photonics Research Centre, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong China. eefaisal.nadeem@eng.usm.my
Faisal Nadeem
FN
Khan
Yudi
Y
Zhou
Alan Pak Tao
AP
Lau
Chao
C
Lu
D000465:Algorithms; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005336:Fiber Optic Technology / Q000295:instrumentation*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D013685:Telecommunications / Q000295:instrumentation*