Prediction of Class-Amplifiers with the Aid of Neural Network

This paper presents a strategy addressing the problem of selection of the class of the amplifiers to be used in future wireless communication systems. The proposed methodology uses a scheme based on neural networks (NN): the characteristics of each class of amplifier (i.e., A, B, AB, C, D, F, G, J, S, T , etc.) are determined and then the ‘classification NN’ is constructed for distinguishing various classes from each other. To validate the method, firstly the designs of various class-amplifiers are collected from the recently published literature, and then the specifications of the amplifiers are extracted in terms of voltage (V), current (I) and efficiency; finally with these data the classification NN is trained. After building this black-box NN, providing the required specifications of each amplifier, designer are informed about the class of amplifier that is predicated by the classification NN and that better fits the characteristics of the considered application. This methodology is important as it leads the way of amplifier class selection in the complex communication systems.