# Gradient-Adaptive Spline-Interpolated LUT Methods for Low-Complexity Digital Predistortion

## TL;DR

This paper introduces gradient-adaptive spline-interpolated lookup table methods for digital predistortion, achieving near state-of-the-art linearization with significantly reduced processing complexity suitable for 5G systems.

## Contribution

It proposes two novel spline-based DPD methods with gradient-based learning algorithms, offering a low-complexity alternative to traditional solutions for PA linearization in 5G.

## Key findings

- Successful linearization of 5G NR PA samples and 28 GHz antenna array
- Performance close to traditional MP DPD with lower complexity
- Effective modeling of PA memory effects using spline-interpolated LUTs

## Abstract

In this paper, new digital predistortion (DPD) solutions for power amplifier (PA) linearization are proposed, with particular emphasis on reduced processing complexity in future 5G and beyond wideband radio systems. The first proposed method, referred to as the spline-based Hammerstein (SPH) approach, builds on complex spline-interpolated lookup table (LUT) followed by a linear finite impulse response (FIR) filter. The second proposed method, the spline-based memory polynomial (SMP) approach, contains multiple parallel complex spline-interpolated LUTs together with an input delay line such that more versatile memory modeling can be achieved. For both structures, gradient-based learning algorithms are derived to efficiently estimate the LUT control points and other related DPD parameters. Large set of experimental results are provided, with specific focus on 5G New Radio (NR) systems, showing successful linearization of multiple sub-6 GHz PA samples as well as a 28 GHz active antenna array, incorporating channel bandwidths up to 200 MHz. Explicit performance-complexity comparisons are also reported between the SPH and SMP DPD systems and the widely-applied ordinary memory-polynomial (MP) DPD solution. The results show that the linearization capabilities of the proposed methods are very close to that of the ordinary MP DPD, particularly with the proposed SMP approach, while having substantially lower processing complexity.