METHOD OF EQUALIZING WAVEFORM DISTORTION, TERMINAL DEVICE, AND OPTICAL COMMUNICATION SYSTEM

    公开(公告)号:US20240031026A1

    公开(公告)日:2024-01-25

    申请号:US17870862

    申请日:2022-07-22

    CPC classification number: H04B10/2507

    Abstract: An optical communication system includes a first terminal device configured to receive first data, wherein the first terminal device is configured to generate an optical waveform based on the received first data. The optical system further includes an optical communication path configured to receive the optical waveform from the first terminal device. The optical system further includes a second terminal device configured to receive the optical waveform from the optical communication path, wherein the second terminal device is configured to output second data based on the optical waveform. At least one of the first terminal device or the second terminal device includes a nonlinear waveform distortion compensation device. The nonlinear waveform compensation device is configured to correct nonlinear waveform distortion resulting from the optical waveform propagating along the optical communication path, and the nonlinear waveform compensation device includes at least one recursive intermediate layer.

    TRAINING APPARATUS, CONTROL METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM

    公开(公告)号:US20240235692A1

    公开(公告)日:2024-07-11

    申请号:US18563461

    申请日:2021-05-28

    CPC classification number: H04B10/61

    Abstract: A training apparatus (2000) acquire a first transmission symbol sequence and a second transmission symbol sequence. The first transmission symbol sequence is input to an optical transmission unit (112), and converted into an optical transmission signal. The second transmission symbol sequence is acquired by demodulating the optical transmission signal. The training apparatus (2000) executes a training of a set of a generator (200) and a discriminator (300) using a training dataset (10) that is generated based on the first and second transmission symbol sequence. The generator (200) is trained so as to generate a data that is determined as being the ground truth data by the discriminator (300). The discriminator (300) is trained so as to distinguish the ground truth data and the data generated by the generator (200). The training apparatus (2000) outputs parameter information (20) that includes trainable parameters of the generator (200).

    CALIBRATION APPARATUS, CALIBRATION METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM

    公开(公告)号:US20250070877A1

    公开(公告)日:2025-02-27

    申请号:US18726472

    申请日:2022-01-18

    Abstract: A calibration apparatus trains a first machine learning-based model with a first training dataset to determine configuration parameters of an intermediate pre-distortion compensator in an optical communication system that includes a transmitter, a receiver, and an optical communication channel. The transmitter includes a pre-distortion compensator, the intermediate pre-distortion compensator, and an MZM compensator. The calibration apparatus trains a second machine learning-based model with a second training dataset to determine configuration parameters of the post-distortion compensator in the receiver. The calibration apparatus trains a third machine learning-based model with a third training dataset to determine configuration parameters of the pre-distortion compensator. When generating the second training data, the intermediate pre-distortion compensator is configured with the configuration parameters generated using the first machine learning-based model. When generating the third training data, the post-distortion compensator is configured with the configuration parameters generated using the second machine learning-based model.

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