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Optimized adaptation algorithms for low-complexity adaptive equalizers.

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    This study introduces optimized algorithms for low-complexity real-valued adaptive equalizers (AEQs) in coherent transmissions. These methods significantly reduce power consumption and multiplications while maintaining performance.

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    Area of Science:

    • Optical communications
    • Signal processing

    Background:

    • Conventional multi-input and multi-output (MIMO) adaptive equalizers (AEQs) in short-reach coherent transmissions exhibit high power consumption.
    • Existing low-complexity AEQs offer partial solutions but may have limitations.

    Purpose of the Study:

    • To analyze optimized adaptation algorithms for low-complexity real-valued (RV) AEQs.
    • To present an approach for efficient optimized adaptation without added computational complexity.
    • To experimentally validate the performance and efficiency of the proposed algorithms.

    Main Methods:

    • Analysis of optimized adaptation algorithms for various RV AEQ structures.
    • Development of a method to integrate optimized adaptation without increasing computational load.
    • Experimental demonstration in a 25 Gbaud dual-polarization 16-quadrature-amplitude-modulation (DP-16QAM) back-to-back intradyne system.

    Main Results:

    • The proposed optimized adaptation algorithms achieve performance comparable to conventional AEQs.
    • A reduction of up to approximately 65% in multiplications was achieved.
    • The approach successfully avoids introducing additional computational complexity.

    Conclusions:

    • Optimized adaptation algorithms offer a viable solution for reducing power consumption in AEQs for short-reach coherent systems.
    • The proposed methods provide significant computational savings without compromising signal quality.
    • This work contributes to more energy-efficient optical communication systems.