Reference signal sample data and channel information sample data acquisition method and apparatus, and device, medium, chip, product and program

An electronic device, comprising:

• a processor;
• and
• a memory for storing computer programs that, when executed by the processor, cause the processor to acquire reference signal sample data and channel information sample data that are generated by a generative model, the generative model being obtained through training based on a Generative Adversarial Network (GAN), real reference signals and real channel information,
• wherein the reference signal sample data and the channel information sample data are used for training a channel estimation model, and the channel estimation model is used to perform channel estimation or channel recovery in a wireless communication system
• wherein the generative model comprises a first generative model and a second generative model,
• wherein the processor is configured to acquire first data generated by the first generative model and input the first data into the second generative model to acquire second data generated by the second generative model,
• wherein the first generative model is a reference signal generative model and the second generative model is a channel generative model, or the first generative model is a channel generative model and the second generative model is a reference signal generative model, the reference signal generative model being used for generating the reference signal sample data, and the channel generative model being used for generating the channel information sample data,
• wherein the processor is configured to:
• input fourth data into a to-be-trained second generative model, and generate fifth data through the to-be-trained second generative model, wherein the fourth data is data generated by the first generative model;
• input the fifth data into a second discriminative model, and output a second discriminative result through the second discriminative model, wherein the second discriminative result is used for discriminating a probability that a category corresponding to combined data is a category to which second real data belongs, the combined data comprises the fourth data and the fifth data, and the second real data comprises the real reference signals and the real channel information;
• and
• adjust model parameters of the to-be-trained second generative model based on the second discriminative result, to obtain the second generative model, wherein a probability that a category corresponding data formed by combining the data generated by the second generative model with the fourth data is the category to which the second real data belongs is greater than a third threshold value.

A chip, comprising:

• a processor, configured to invoke and run computer programs from a memory to cause an electronic device on which the chip is mounted to acquire reference signal sample data and channel information sample data that are generated by a generative model, the generative model being obtained through training based on a Generative Adversarial Network (GAN), real reference signals and real channel information,
• wherein the reference signal sample data and the channel information sample data are used for training a channel estimation model, and the channel estimation model is used to perform channel estimation or channel recovery in a wireless communication system,
• wherein the generative model comprises a first generative model and a second generative model,
• wherein the processor is configured to acquire first data generated by the first generative model and input the first data into the second generative model to acquire second data generated by the second generative model,
• wherein the first generative model is a reference signal generative model and the second generative model is a channel generative model, or the first generative model is a channel generative model and the second generative model is a reference signal generative model, the reference signal generative model being used for generating the reference signal sample data, and the channel generative model being used for generating the channel information sample data,
• wherein the processor is configured to:
• input fourth data into a to-be-trained second generative model, and generate fifth data through the to-be-trained second generative model, wherein the fourth data is data generated by the first generative model:
• input the fifth data into a second discriminative model, and output a second discriminative result through the second discriminative model, wherein the second discriminative result is used for discriminating a probability that a category corresponding to combined data is a category to which second real data belongs, the combined data comprises the fourth data and the fifth data, and the second real data comprises the real reference signals and the real channel information;
• and
• adjust model parameters of the to-be-trained second generative model based on the second discriminative result, to obtain the second generative model, wherein a probability that a category corresponding data formed by combining the data generated by the second generative model with the fourth data is the category to which the second real data belongs is greater than a third threshold value.