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Computational complexity comparison of feedforward/radial basis function/recurrent neural network-based equalizer for

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    Summary

    Auto-regressive recurrent neural networks (AR-RNN) offer the best bit-error-rate (BER) performance and lowest computational complexity for 50-Gb/s pulse amplitude modulation (PAM)-4 direct-detection optical links. This research guides selection of neural network equalizers for real-time applications.

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    Area of Science:

    • Optical communications
    • Signal processing
    • Artificial intelligence

    Background:

    • High-speed optical links like 50-Gb/s pulse amplitude modulation (PAM)-4 direct-detection (DD) systems require advanced equalization techniques to mitigate signal impairments.
    • Neural network (NN)-based nonlinear equalizers are emerging as a promising solution for improving bit-error-rate (BER) performance.

    Purpose of the Study:

    • To analyze and compare the computational complexity and BER performance of four types of NN-based nonlinear equalizers.
    • To provide guidelines for selecting appropriate NN-based equalizers for optical communication systems based on specific requirements.

    Main Methods:

    • Analysis of computational complexity and BER performance for feedforward neural networks (F-NN), radial basis function neural networks (RBF-NN), auto-regressive recurrent neural networks (AR-RNN), and layer-recurrent neural networks (L-RNN).
    • Numerical simulations were conducted for a 50-Gb/s PAM-4 DD optical link.

    Main Results:

    • AR-RNN-based equalizers demonstrated the lowest computational complexity for a fixed BER threshold.
    • Amongst equalizers with identical input and hidden neuron counts, F-NNs had the lowest complexity, while AR-RNNs achieved the best BER performance.
    • RBF-NNs require more hidden neurons with increasing input numbers, limiting their suitability for long-distance transmissions.
    • NN-based equalizers require only tens of multiplications per symbol for good BER performance, enabling real-time implementation.

    Conclusions:

    • AR-RNNs present an optimal balance of low computational complexity and superior BER performance for 50-Gb/s PAM-4 DD optical links.
    • The study offers valuable insights for engineers to select NN-based equalizers tailored to specific BER and computational constraints in optical systems.