{'fraction_non_alphanumeric': 0.025793650793650792, 'fraction_numerical': 0.0, 'mean_word_length': 6.006944444444445, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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.