The invention relates to a digital pre-
distortion method and
system based on a hierarchical iterative neural network, and belongs to the field of digital pre-
distortion of power amplifiers. According to the method, the pre-
distortion process is decoupled into two stages of off-line generalization training and on-line specialization optimization. In the offline stage, a mixed
data set containing multiple modulation
modes and bandwidths is adopted to
train an offline neural network, and a basic model with high generalization ability is constructed; in the online stage, aiming at a
signal with a specific modulation mode and bandwidth, a single configuration
data set is utilized to
train an online neural network, and the accurate compensation capability of the model on a real-time input
signal is enhanced. In practical application, a
baseband signal is firstly subjected to targeted preprocessing through the online neural network, then is subjected to deep optimization through the offline neural network, and finally is input into the power
amplifier PA. According to the method, through a generalization-specialization double-layer structure, the dynamic response capability and compensation precision of a digital pre-distortion
system to a complex signal environment are remarkably improved.