Calibration apparatus, calibration method, and non-transitory computer-readable storage medium

A calibration method performed by a computer, comprising:

• training 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 including a pre-distortion compensator, the intermediate pre-distortion compensator, and a Mach Zehnder Modulator (MZM) compensator, the first training dataset including multiple pairs of:
• a first input data that represents a transmission symbol sequence representing a message to be sent;
• and a first ground-truth data that represents an inverse signal of distortion that is generated based on an output from an electrical path in the transmitter, the output from the electrical path being acquired by feeding the transmission symbol sequence to the transmitter in which the intermediate pre-distortion compensator and the MZM compensator are enabled while the pre-distortion compensator is disabled;
• training a second machine learning-based model with a second training dataset to determine configuration parameters of the post-distortion compensator included in the receiver, the second training dataset including multiple pairs of:
• a second input data that represents an output from an optical front end included in the receiver;
• and a second ground-truth data that represents the transmission symbol sequence, the output from the optical front end being acquired by feeding the transmission symbol sequence to the optical communication system in which the intermediate pre-distortion compensator, the MZM compensator, and the post-distortion compensator are enabled while the pre-distortion compensator is disabled and in which the configuration parameters determined using the first machine learning-based model are applied to the intermediate pre-distortion compensator;
• and
• training a third machine learning-based model with a third training dataset to determine configuration parameters of the pre-distortion compensator, the third training dataset including multiple pairs of:
• a third input data that represents the transmission symbol sequence;
• and a third ground-truth data that represents an inverse signal of distortion that is generated based on an output from the post-distortion compensator, the output from the post-distortion compensator being acquired by feeding the transmission symbol sequence to the optical communication system in which the pre-distortion compensator and the post-distortion compensator are enabled while the intermediate pre-distortion compensator and the MZM compensator are disabled and in which the configuration parameters determined using the second machine learning-based model are applied to the post-distortion compensator.

A non-transitory computer-readable storage medium storing a program that causes a computer to execute:

• training 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 including a pre-distortion compensator, the intermediate pre-distortion compensator, and a Mach Zehnder Modulator (MZM) compensator, the first training dataset including multiple pairs of:
• a first input data that represents a transmission symbol sequence representing a message to be sent;
• and a first ground-truth data that represents an inverse signal of distortion that is generated based on an output from an electrical path in the transmitter, the output from the electrical path being acquired by feeding the transmission symbol sequence to the transmitter in which the intermediate pre-distortion compensator and the MZM compensator are enabled while the pre-distortion compensator is disabled;
• training a second machine learning-based model with a second training dataset to determine configuration parameters of the post-distortion compensator included in the receiver, the second training dataset including multiple pairs of:
• a second input data that represents an output from an optical front end included in the receiver;
• and a second ground-truth data that represents the transmission symbol sequence, the output from the optical front end being acquired by feeding the transmission symbol sequence to the optical communication system in which the intermediate pre-distortion compensator, the MZM compensator, and the post-distortion compensator are enabled while the pre-distortion compensator is disabled and in which the configuration parameters determined using the first machine learning-based model are applied to the intermediate pre-distortion compensator;
• and
• training a third machine learning-based model with a third training dataset to determine configuration parameters of the pre-distortion compensator, the third training dataset including multiple pairs of:
• a third input data that represents the transmission symbol sequence;
• and a third ground-truth data that represents an inverse signal of distortion that is generated based on an output from the post-distortion compensator, the output from the post-distortion compensator being acquired by feeding the transmission symbol sequence to the optical communication system in which the pre-distortion compensator and the post-distortion compensator are enabled while the intermediate pre-distortion compensator and the MZM compensator are disabled and in which the configuration parameters determined using the second machine learning-based model are applied to the post-distortion compensator.