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

arXiv: https://arxiv.org/abs/2402.09964 · HTML: https://mlsanity.com/paper/2402.09964/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2024.

## Built on by (most cited first, 2 of 2)

- [Inter-Cell Interference Rejection Based on Ultrawideband Walsh-Domain Wireless Autoencoding](https://mlsanity.com/paper/2601.11713/) (2026)
- [Experimental Evaluation of a Low-Power Ultra-Wideband Receiver for Spectrum Sensing](https://mlsanity.com/paper/2609.22134/) (2026)

## Abstract (arXiv)

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.