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    Area of Science:

    • Optical Communications
    • Signal Processing
    • Machine Learning

    Background:

    • Bandlimited channels in high-speed optical systems cause severe inter-symbol interference (ISI).
    • Existing equalization methods often require perfect channel state information, which can be difficult to obtain.
    • Recurrent and feedforward neural networks are explored for mitigating ISI.

    Purpose of the Study:

    • To propose and evaluate a novel deep bidirectional long short-term memory (BiLSTM) architecture for mitigating ISI.
    • To compare the performance of the BiLSTM against traditional methods like maximum likelihood sequence estimation (MLSE) and linear equalization.
    • To assess the BiLSTM's performance under various ISI conditions, including high-speed optical channels.

    Main Methods:

    • Development of a deep bidirectional long short-term memory (BiLSTM) neural network architecture.
    • Simulations were conducted for Quadrature Phase Shift Keying (QPSK) transmission over channels with varying ISI.
    • Performance was evaluated using bit error rate (BER) metrics, comparing BiLSTM against MLSE and linear equalization.

    Main Results:

    • The deep BiLSTM achieved optimal bit error rate (BER) performance, matching that of MLSE with perfect channel knowledge for QPSK.
    • Performance degraded with increased modulation order and ISI severity but still significantly outperformed linear equalization.
    • The BiLSTM required no channel state information, demonstrating comparable performance to conventional equalizers with perfect channel knowledge.

    Conclusions:

    • Deep BiLSTM networks offer a powerful, data-driven approach to mitigating severe ISI in high-speed optical communications.
    • The proposed architecture achieves near-optimal performance without explicit channel estimation, simplifying practical implementation.
    • This neural network approach presents a viable alternative to conventional equalization techniques, especially in complex channel conditions.