Abstract
A new learning-based structure is defined for the identification of nonlinear attributes of a baseband power amplifier (PA) and design of an adaptive digital pre-distorter for application in wireless channels. Analytically and experimentally, generalized memory polynomial (GMP) model is derived from the transmit–receive conditions and the learning network structure. The test data are a sequences of 16-quadrature amplitude modulation (QAM) and 64-QAM complex symbols. In simulation, unknown GMP models of different order are used as a baseband PA model for identification. Minicircuits ZHL-4240 radio frequency PA modules along with NI USRP 2920 units are employed for experimental verification of the concept. The learning aspect is governed by a specially designed artificial neural network structure called real valued focused time delay neural network. Simulation and empirical results agree with analytical derivation.
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