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Complex-valued recurrent neural network equalizer with low complexity for a 120-Gbps 50-km optical PAM-4 IM/DD system
Optics Express
|November 14, 2024
Summary
A new complex-valued recurrent neural network equalizer (RNNE) improves optical communication systems. This novel equalizer enhances performance in 120-Gbps PAM-4 intensity modulation and direct detection systems.
Area of Science:
- Optical Communications
- Signal Processing
- Artificial Intelligence
Background:
- Intensity Modulation and Direct Detection (IM/DD) systems are crucial for high-speed optical networks.
- 4-level Pulse Amplitude Modulation (PAM-4) is widely used to increase data rates.
- Traditional equalizers struggle with the complex signal characteristics in IM/DD systems.
Purpose of the Study:
- To introduce a novel complex-valued recurrent neural network equalizer (RNNE).
- To evaluate the performance of the complex-valued RNNE in a 120-Gbps, 50-km optical PAM-4 IM/DD system.
- To demonstrate the advantages of complex-valued processing for real-valued signals in IM/DD systems.
Main Methods:
- Development of a complex-valued recurrent neural network equalizer (RNNE).
- Mapping adjacent PAM-4 symbols to the complex domain to preserve signal correlation.
- Experimental validation of the complex-valued RNNE against a traditional real-valued RNNE.
Main Results:
- The proposed complex-valued RNNE demonstrated superior performance compared to the real-valued RNNE.
- A system power budget gain of 1.38 dB was achieved at the 7% overhead forward error correction BER threshold of 3.8 × 10^-3.
- Complex-valued processing proved advantageous for real-valued signals in IM/DD systems.
Conclusions:
- Complex-valued RNNE offers significant improvements for high-speed optical IM/DD systems.
- The proposed method effectively preserves signal correlation by utilizing the complex domain.
- Complex-valued RNNE is a promising technique for future optical communication equalization.
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