Walsh-domain Neural Network for Power Amplifier Behavioral Modelling and Digital Predistortion

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 ultrahigh bandwidth, low-power Walsh transceivers.