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Nonlinearity of power amplifier is one of the major limitations to theachievable capacity in wireless transmission systems. Nonlinear impairments aredetermined by the nonlinear distortions of the power amplifier and modulatorimperfections. The Volterra model, several compact Volterra models and neuralnetwork models to establish a nonlinear model of power amplifier have all beendemonstrated. However, the computational cost of these models increases andtheir implementation demands more signal processing resources as the signalbandwidth gets wider or the number of carrier aggregation. A completelydifferent approach uses deep convolutional neural network to learn from thetraining data to figure out the nonlinear distortion. In this work, a lowcomplexity, general architecture based on the deep real-valued convolutionalneural network (DRVCNN) is proposed to build the nonlinear behavior of thepower amplifier. With each of the multiple inputs equivalent to an inputvector, the DRVCNN tensor weights are constructed from training data thanks tothe current and historical envelope-dependent terms, I, and Q, which arecomponents of the input. The effectiveness of the general framework in modelingsingle-carrier and multi-carrier power amplifiers is verified.

A General Architecture for Behavior Modeling of Nonlinear Power Amplifier using Deep Convolutional Neural Network