Utilizing Sparse-Aware Volterra for Power Amplifier Behavioral Modeling

This paper presents a method for reducing the number of weights in a time series behavioral model for a power amplifier. The least-absolute shrinkage and selection operator (Lasso) algorithm is used to reduce the kernel size, preserving the important kernels, while eliminating the less important kernels. The algorithm is evaluated on a behavioral model for a class AB amplifier, the algorithm reduces the number of weights by greater than 70% without degrading model performance by a significant amount.

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