Selection of compressed training data for RF power amplifier behavioral modeling

In this paper, we present an algorithm which uses the probability information of the input signal to inform the selection of a compressed training dataset for RF PA behavioural model extraction. The proposed algorithm can dramatically reduce the number of training samples. The accuracy of this algorithm is validated by extraction of behavioural models using a large dataset of consecutive samples and a reduced training dataset determined using the proposed algorithm. A noticeable reduction in computational complexity and faster execution time is achieved with the new approach.