Intelligent digital pre-distortion system and method for dynamic transmission
A digital pre-distortion and dynamic technology, applied in the direction of synchronization/start-stop system, biological neural network model, neural architecture, etc., can solve problems such as inefficiency, reduce the number of coefficients, increase flexibility, improve stability and convergence speed Effect
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Embodiment 1
[0061] see Figure 1-Figure 4 , this embodiment provides an intelligent digital pre-distortion system oriented to dynamic transmission, including: a polynomial auxiliary module, a signal feature estimation module, and a neural network module; wherein,
[0062] The polynomial auxiliary module uses the prior information of the power amplifier to eliminate the static nonlinearity of the power amplifier in dynamic transmission scenarios;
[0063] The signal feature estimation module estimates the signal feature of the dynamic transmission signal, and inputs the estimated signal feature together with the input signal into the neural network module;
[0064] The neural network module compensates the dynamic nonlinearity of the power amplifier caused by the change of the input signal according to the acquired signal characteristics and the input signal.
[0065] Specifically, in this embodiment, the signal feature estimation module extracts a recent input signal by using a finite im...
Embodiment 2
[0096] This embodiment provides an intelligent digital pre-distortion method for dynamic transmission on the basis of Embodiment 1, including:
[0097] Step S1, input multiple sets of input signals with different characteristics required for dynamic transmission into the power amplifier, and define the input signals as original input signals, then obtain multiple sets of first output signals from the output end of the power amplifier, and then extract All the features of the original input signal, and construct a data set with the original input signal, the first output signal and the features, and then divide the data set into a training set and a verification set in a certain proportion;
[0098] Step S2, selecting the size of the finite impulse response window in the signal feature extraction module and the traditional model that needs to be embedded in the polynomial auxiliary module, and setting the hyperparameters of the modeling system;
[0099] Specifically, in this em...
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