Iterative Learning Control Based Digital Pre-Distortion for Mitigating Impairments in MIMO Wireless Transmitters

Digital pre-distortion (DPD) has recently been developed to compensate for in-phase and quadrature (IQ) imbalance and crosstalk, as well as power amplifier (PA) nonlinearity distortions in multi-input multi-output (MIMO) transmitters (TXs). Despite its limitations, most DPD models still use a simple non-iterative framework called the indirect learning architecture (ILA). This paper proposes a novel integrated DPD solution supported by iterative learning control (ILC) and a neural network (NN) model to compensate for all of these impairments simultaneously. Compared to the state-of-the-art DPD models, our proposed scheme achieves excellent in-band and out-of-band (OOB) performance. In addition, it has a significantly lower running complexity than other polynomial-based models, with 50% fewer floating-point operations (FLOPs).

Automated summary

This paper proposes a novel integrated DPD solution supported by iterative learning control (ILC) and a neural network (NN) model to compensate for IQ imbalance, crosstalk, and PA nonlinearity in MIMO transmitters. Our scheme achieves excellent performance in-band and out-of-band, and has 50% lower running complexity compared to other polynomial-based models.