Temperature behavior modeling based on resilient BPNN for a GaAs pHEMT high gain MMIC PA

Abstract

AbstractAs the key component of transceiver, power amplifier (PA) plays an extremely important role in wireless communication system. In order to accurately characterize the performance variation of PA at any temperature, the temperature behavior of a gallium arsenide (GaAs) pseudomorphic high electron mobility transistor (pHEMT) high gain monolithic microwave integrated circuit (MMIC) PA is modeled in this paper. In this modeling, the resilient back propagation neural network (BPNN) is utilized to do the temperature behavior modeling for this PA. The investigation shows that the minimum mean square error (MSE) of the prediction results is 4.9607 × 10−4, which implies that it is feasible to use this modeling method to characterize the temperature behavior of PA. This modeling not only theoretically overcomes the problem of slow convergence speed of BPNN, but also solves the limitation of experimental devices and time setting. It will provide a more convenient guidance for test engineers.