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We investigate neural network (NN) assisted techniques for compensating thenon-linear behaviour and the memory effect of a 5G PA through digitalpredistortion (DPD). Traditionally, the most prevalent compensation techniquecomputes the compensation element using a Memory Polynomial Model (MPM).Various neural network proposals have been shown to improve on thisperformance. However, thus far they mostly come with prohibitive training orinference costs for real world implementations. In this paper, we propose a DPDarchitecture that builds upon the practical MPM formulation governed by neuralattention. Our approach enables a set of MPM DPD components to individuallylearn to target different regions of the data space, combining their outputsfor a superior overall compensation. Our method produces similar performance tothat of higher capacity NN models with minimal complexity. Finally, we view ourapproach as a framework that can be extended to a wide variety of localcompensator types.

Efficient attention guided 5G power amplifier digital predistortion