Intelligent digital predistortion system and method for dynamic transmission
A digital predistortion and dynamic technology, applied in synchronization/start-stop system, biological neural network model, neural architecture, etc., can solve problems such as inefficiency, reduce the number of coefficients, improve stability and convergence speed, and reduce the amount of calculation. Effect
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Embodiment 1
[0061] see Figure 1-Figure 4 , this embodiment provides a dynamic transmission-oriented intelligent digital predistortion system, 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 the dynamic transmission scenario;
[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 impu...
Embodiment 2
[0096] This embodiment provides a dynamic transmission-oriented intelligent digital predistortion method 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 the input signal is defined as the original input signal, 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 based on the original input signal, the first output signal and the feature, and then divide the data set into a training set and a verification set in a certain proportion;
[0098] Step S2, select 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 set the hyperparameters of the modeling system;
[0099] Specifically, in th...
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