  This paper investigates the use of Neural Network (NN) nonlinear modelling
for Power Amplifier (PA) linearization in the Walsh-Hadamard transceiver
architecture. This novel architecture has recently been proposed for ultra-high
bandwidth systems to reduce the transceiver power consumption by extensive
parallelization of the digital baseband hardware. The parallelization is
achieved by replacing two-dimensional quadrature modulation with
multi-dimensional Walsh-Hadamard modulation. The open research question for
this architecture is whether conventional baseband signal processing algorithms
can be similarly parallelized while retaining their performance. A key baseband
algorithm, digital predistortion using NN models for PA linearization, will be
adapted to the parallel Walsh architecture. A straighforward parallelization of
the state-of-the-art NN architecture is extended with a cross-domain Knowledge
Distillation pre-training method to achieve linearization performance on par
with the quadrature implementation. This result paves the way for the entire
baseband processing chain to be adapted into ultra-high bandwidth, low-power
Walsh transceivers.
