Measurement results for compensation of impairments of LTE stimulated direct‐conversion transmitter through augmented MoE

This work presents a neural network-based one-step solution for modeling predistorter and mitigating the nonlinear distortion of PA along with impairments, I/Q imbalance, and DC offset of the direct conversion transmitter for wide-band signals. In this work, a modified form of Mixture of experts (MoE), augmented MoE is used as a predistorter to linearize direct conversion transmitter. MoE model encompasses a family of modular neural network architectures having several expert networks connected to a single gating network, and it follows the Divide-and-Conquer principle. The direct-conversion transmitter with class AB-PA was stimulated with a wide-band three carriers Long-Term Evolution (LTE) with different bandwidth signals. The presented method's measurement results show that the proposed model has good linearization performance in the presence of transmitter impairments.