Patent ID: 11949464
Assignee: SHENZHEN RESEARCH INSTITUTE OF THE HONG KONG POLYTECHNIC UNIVERSITY
Field: Telecommunications (Electrical engineering)
Classification: CPC H | IPC H

Claim 1:
2. The noise processing method according to claim 1, wherein calculating the X noise correlation coefficient on the X polarization state and the Y noise correlation coefficient on the Y polarization state respectively, the X noise correlation coefficient is configured to characterize the correlation between the training eigenvalue noise of the training discrete spectral eigenvalue and the X training discrete spectral coefficient noise of the X training discrete spectral coefficient on the X polarization state; the Y noise correlation coefficient is configured to characterize a correlation between the training eigenvalue noise and a Y training discrete spectral coefficient noise of a Y training discrete spectral coefficient on the Y polarization state; the training discrete spectral eigenvalue and the training discrete spectral coefficient are two properties of the training nonlinear frequency division multiplexing signal in the nonlinear frequency domain; the X polarization state and the Y polarization state are two polarization states of the training nonlinear frequency division multiplexing signal in the time domain, comprising:
obtaining an X training eigenvalue noise and a Y training eigenvalue noise in the training eigenvalue noises according to the training eigenvalue noise; the X training eigenvalue noise is configured to characterize a noise contained in the training discrete spectral eigenvalue located on the X polarization state; the Y training eigenvalue noise is configured to characterize a noise contained in the training discrete spectral eigenvalue located on the Y polarization state;
circularly shifting the X training eigenvalue noise and the Y training eigenvalue noise by K positions respectively, where K is an integer, and obtaining the X training eigenvalue noise and the Y training eigenvalue noise after the shift respectively;
constructing a training noise matrix according to an arrangement order of the X training eigenvalue noise, the X training eigenvalue noise after the shift, the Y training eigenvalue noise, and the Y training eigenvalue noise after the shift;
obtaining the X noise correlation coefficient and the Y noise correlation coefficient according to the training noise matrix, the X training discrete spectral coefficient noise, and the Y training discrete spectral coefficient noise.