Channel estimation method based on compressed sensing and deep learning, medium and equipment

The invention discloses a channel estimation method based on compressed sensing and deep learning, a medium and equipment. The method comprises the steps that a base station end of an orthogonal frequency division multiplexing system sends a signal to a user end in a downlink in a comb-shaped pilot frequency form; the user side in the downlink obtains the sent pilot frequency receiving signal y and feeds back the signal y to the base station side; the base station end of the orthogonal frequency division multiplexing system performs channel estimation based on an ASJOMP algorithm of compressed sensing according to the obtained receiving pilot signal y by using the structured sparse characteristic of a time delay domain sparse channel to obtain an initial estimation channel, builds a noise reduction neural network based on deep learning, and trains a DnNet network by using an existing sample to obtain a network parameter theta; and de-noising the obtained initial estimation channel according to the DnNet netwo