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Wideband dynamic behavioral modeling of reflective semiconductor optical amplifiers using a tapped-delay multilayer
Zhansheng Liu1, Manuel Alberto Violas, Nuno Borges Carvalho
1Instituto de Telecomunicações, Dep. Electrónica Telecomunicações e Informática, Universidade de Aveiro, Campus Universitário de Santiago, Aveiro 3810-193, Portugal.
Optics Express
|March 14, 2013
Summary
We developed a dynamic model for reflective semiconductor optical amplifier (RSOA) modulators in radio over fiber systems. This tapped-delay multilayer perceptron model accurately captures RSOA behavior and distortions.
Area of Science:
- Optoelectronics
- Optical Communications
- Signal Processing
Background:
- Reflective Semiconductor Optical Amplifiers (RSOAs) are crucial modulators in colorless Radio over Fiber (RoF) systems.
- Accurate modeling of RSOA dynamic behavior is essential for mitigating nonlinear distortions and improving system performance.
Purpose of the Study:
- To propose and validate a wideband dynamic behavioral model for RSOA modulators.
- To analyze nonlinear distortion and dynamic effects in RSOA modulators using a machine learning approach.
Main Methods:
- Utilizing a tapped-delay multilayer perceptron (TDMLP) for dynamic RSOA modeling.
- Training, validating, and testing the TDMLP model with 64 quadrature amplitude modulation (QAM) signals at 20 Msymbol/s.
- Optimizing TDMLP parameters (hidden layer nodes, memory depth) for accuracy and generality.
Main Results:
- The TDMLP model achieved a Normalized Mean Square Error (NMSE) of up to -44.33 dB with optimized parameters (20 hidden nodes, 3 memory depth).
- Demonstrated the model's ability to accurately approximate RSOA dynamic characteristics, including AM-AM and AM-PM distortions.
- Confirmed that a single hidden layer TDMLP effectively captures RSOA modulator behaviors.
Conclusions:
- The proposed TDMLP model offers a highly accurate and general approach for characterizing RSOA dynamic behavior in RoF systems.
- This modeling technique is effective in analyzing and potentially mitigating nonlinear distortions inherent in RSOA modulators.
- The study highlights the capability of advanced machine learning models in optimizing optical communication system components.
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