  Nonlinearity of power amplifier is one of the major limitations to the
achievable capacity in wireless transmission systems. Nonlinear impairments are
determined by the nonlinear distortions of the power amplifier and modulator
imperfections. The Volterra model, several compact Volterra models and neural
network models to establish a nonlinear model of power amplifier have all been
demonstrated. However, the computational cost of these models increases and
their implementation demands more signal processing resources as the signal
bandwidth gets wider or the number of carrier aggregation. A completely
different approach uses deep convolutional neural network to learn from the
training data to figure out the nonlinear distortion. In this work, a low
complexity, general architecture based on the deep real-valued convolutional
neural network (DRVCNN) is proposed to build the nonlinear behavior of the
power amplifier. With each of the multiple inputs equivalent to an input
vector, the DRVCNN tensor weights are constructed from training data thanks to
the current and historical envelope-dependent terms, I, and Q, which are
components of the input. The effectiveness of the general framework in modeling
single-carrier and multi-carrier power amplifiers is verified.
