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    We developed an adaptive optical self-interference cancellation method for full-duplex systems. This regular triangle algorithm automatically optimizes cancellation, reducing interference by 20-25 dB adaptively.

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    Area of Science:

    • Optical Communications
    • Signal Processing

    Background:

    • Full-duplex systems enable simultaneous data transmission and reception.
    • Self-interference is a major challenge in achieving efficient full-duplex communication.
    • Existing cancellation methods often require manual adjustments and lack adaptive capabilities.

    Purpose of the Study:

    • To propose an adaptive optical self-interference cancellation algorithm for in-band full-duplex systems.
    • To replace manual adjustments with an automated, adaptive process for optimal interference cancellation.
    • To enhance the efficiency and performance of full-duplex communication systems.

    Main Methods:

    • Developed a regular triangle algorithm for adaptive optical self-interference cancellation.
    • Simplified the adjustment process into a convex function problem.
    • Continuously sampled post-cancellation signal power to adjust optical time delay and attenuation.
    • Achieved automatic matching of the two paths in the self-interference cancellation system.

    Main Results:

    • Achieved adaptive cancellation of a 300-MHz wideband interference signal down to the noise floor.
    • Demonstrated an adaptive cancellation depth of 20-25 dB.
    • The proposed algorithm reached the optimal cancellation point faster than existing methods.
    • Required 10-30% fewer samples from near start regions and 40-60% fewer samples from farther start regions compared to other algorithms.

    Conclusions:

    • The regular triangle algorithm provides effective and adaptive optical self-interference cancellation for full-duplex systems.
    • The automated approach simplifies system setup and improves cancellation performance.
    • This method offers significant improvements in convergence speed and sample efficiency over existing algorithms.