Dual-band Transmitter Linearization using Spiking Neural Networks

In this paper, a novel way for dual-band power amplifiers (PA) linearization is proposed based on spiking neuron networks (SNN). A dual-input dual-output SNN is interacted with classical memory polynomial model, which can largely reduce the computational complexity. The experimental results on a real PA show that the proposed method can reach similar linearization performance compared with traditional methods but with low energy consumption. This is the first time that the SNN is deployed for multi-band PA linearization. Future work is to develop a trainable SNN model for real-time PA linearization.