  Neural networks (NNs) for multiple hardware impairments mitigation of a
realistic direct conversion transmitter are impractical due to high
computational complexity. We propose two methods to reduce complexity without
significant performance penalty. We first propose a novel attention residual
learning NN, referred to as attention residual real-valued time-delay neural
network (ARDEN), where trainable neuron-wise shortcut connections between the
input and output layers allow to keep the attention always active. Furthermore,
we implement a NN pruning algorithm that gradually removes connections
corresponding to minimal weight magnitudes in each layer. Simulation and
experimental results show that ARDEN with pruning achieves better performance
for compensating frequency-dependent quadrature imbalance and power amplifier
nonlinearity than other NN-based and Volterra-based models, while requiring
less or similar complexity.
