研究目的
To compensate for nonlinear effects in fibre-optic communication links using combined optical signal processing methods.
研究成果
The combined use of PCS modulation and static neural networks enhances signal processing efficiency, reducing bit error rates by three to four times and allowing higher optimal power transmission. This approach partially compensates for nonlinear effects and improves bandwidth utilization in fibre-optic communication links.
研究不足
The use of static neural networks cannot completely compensate for nonlinear effects due to the memory nature of the optical channel. The redundancy in PCS modulation is limited to 10-15% for practical efficiency. Computational complexity and real-time implementation challenges are not fully addressed.
1：Experimental Design and Method Selection:
The study uses a combination of probabilistic constellation shaping (PCS) modulation and static neural networks for signal processing. Numerical simulation is employed to model signal transmission through an optical fibre using the nonlinear Schr?dinger equation solved with the symmetric split-step Fourier method.
2：Sample Selection and Data Sources:
A 16-QAM signal is used as the initial signal. The communication link consists of 10 spans, each with a 100 km fibre segment and an amplifier.
3：List of Experimental Equipment and Materials:
Fibre-optic communication link components including transmitters, receivers, amplifiers, band-pass filters, and neural networks. Specific parameters: attenuation coefficient α =
4：2 dB km?1, fibre nonlinearity γ = 4 W?1 km?1, chromatic dispersion β? = -25 ps2 km?1, wavelength λ = 1550 nm, number of samples per period q = Experimental Procedures and Operational Workflow:
The transmitter generates a 16-QAM signal, processed by a PCS modulator if needed. The signal passes through the fibre link with amplifiers adding noise. At the receiver, it is filtered, and chromatic dispersion and phase shift are compensated; neural networks may be used for further compensation.
5：Data Analysis Methods:
Bit error rate (BER) is measured as a function of input power. Neural network training uses Riedmiller's resilient backpropagation algorithm. Analytical methods based on transformed Schr?dinger equations are used to explain results.
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