Digital Predistortion of 5G Multiuser MIMO Transmitters Using Low-Dimensional Feature-Based Model Generation

Abstract

In this article, we present a novel digital predistortion (DPD) system which can be updated quickly and efficiently in response to the dynamic reconfiguration of multiuser multiple-input multiple-output (MIMO) transmitters. By identifying the shared properties of different power amplifiers (PAs) with two feature extraction stages, nonlinear behaviors of the PAs are encoded into low-dimensional features. Using the extracted features as input, a novel DPD generator is employed to synthesize DPD model coefficients directly. In this way, when the power levels or beam angles change, the DPD model can be updated fast and accurately without capturing the output data or applying linear system identification algorithms. To capture slow-varying dynamics of the PAs, the features can also be calibrated efficiently in the background. Computational complexity and operational latency can thus be reduced significantly. Simulation and experimental results demonstrate that the proposed DPD approach achieves excellent linearization performance with low complexity, making itself a promising linearization solution for 5G massive MIMO transmitters.