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Measurement results for compensation of impairments of LTE stimulated direct‐conversion transmitter through augmented MoE
Author(s) -
Bhatt Manoj,
Tripathi Girish Chandra,
Rawat Meenakshi,
Mathur Sanjay
Publication year - 2021
Publication title -
international journal of rf and microwave computer‐aided engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 39
eISSN - 1099-047X
pISSN - 1096-4290
DOI - 10.1002/mmce.22825
Subject(s) - transmitter , linearization , electronic engineering , computer science , bandwidth (computing) , offset (computer science) , artificial neural network , nonlinear system , telecommunications , engineering , artificial intelligence , physics , channel (broadcasting) , quantum mechanics , programming language
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.