  Digital predistortion is the process of correcting for nonlinearities in the
analog RF front-end of a wireless transmitter. These nonlinearities contribute
to adjacent channel leakage, degrade the error vector magnitude of transmitted
signals, and often force the transmitter to reduce its transmission power into
a more linear but less power-efficient region of the device. Most predistortion
techniques are based on polynomial models with an indirect learning
architecture which have been shown to be overly sensitive to noise. In this
work, we use neural network based predistortion with a novel neural network
training method that avoids the indirect learning architecture and that shows
significant improvements in both the adjacent channel leakage ratio and error
vector magnitude. Moreover, we show that, by using a neural network based
predistorter, we are able to achieve a 42% reduction in latency and 9.6%
increase in throughput on an FPGA accelerator with 15% fewer multiplications
per sample when compared to a similarly performing memory-polynomial
implementation.
