The invention relates to a cloud
wireless access network modeling method based on
machine learning, and the method comprises the steps: obtaining a remote cloud
wireless access network transmitter, improving the remote cloud
wireless access network transmitter, replacing a conventional
microwave source of the remote cloud wireless access network
transmitter with a double-ring photoelectric oscillator, and suppressing a side mode through single-mode oscillation, a direct modulation
laser is introduced behind the
arbitrary waveform generator and is used for generating
power gain by utilizing a
chirp effect and
optical fiber dispersion cooperative
gain; and based on a lightweight gradient
elevator model, modeling a
millimeter wave
signal spectrum of the improved remote cloud wireless access network transmitter, and matching input feature dimensions and output feature dimensions. According to the invention, the dual-ring photoelectric oscillator is adopted to replace a traditional
microwave source, so that
phase noise is reduced; a cooperative
gain behavior of directly modulating
laser chirp and
optical fiber dispersion is utilized to improve
millimeter wave
signal transmission
gain; the spectral characteristics of
millimeter wave signals are modeled through a lightweight gradient
elevator model, and spectrum prediction is realized.