A Committee Machine Neural Network for Dynamic and its Inverse Modeling of Distortions and Impairments in Wireless Transmitters. Issue 4 (19th May 2023)

• Record Type: Journal Article
• Authors: Bhatt, Manoj; Rawat, Meenakshi; Mathur, Sanjay
• Is Part Of: IETE journal of research. Volume 69:Issue 4(2023)
• Page Start: 2025
• Page End: 2036
• Publication Date: 2023-05-19
• Subjects: HPA nonlinearity -- Saleh model -- Class AB-PA -- Predistorter -- Linearization -- MOE; Electronics -- Periodicals; Telecommunication -- Periodicals; Electronics; Telecommunication; Periodicals; 621.38
• DOI: 10.1080/03772063.2021.1884136
• Languages: English
• ISSNs: 0377-2063

Abstract

Abstract : The tradeoff between bandwidth efficiency and power efficiency with nonlinear distortion introduced by high power amplifier (HPA) in a modern communication system, at a high bit rate has become a prominent problem. Digital predistortion (DPD), the most cost-effective and yet lesser complex method is a widely used linearization method. In this method, the dynamic nonlinear characteristics of the PA are used to obtain an inverse transfer function to abolish the nonlinearity. The development of a good DPD technique depends on its ability to model an accurate PA behavior and its inverse behavior. Recently, several neural network-based predistortion linearizers had been proposed, and these linearizers are effectively proving their worth, but these methods report inadequate generalization performance in the presence of system imperfections like DC offset, IQ imbalance, and both. The present paper proposes a Mixture of Experts (MOE) based committee machine neural network for modeling the PA/transmitter system transfer function and its inverse transfer function under different system conditions. MOE models encompass a family of modular neural network architectures having several experts network and a single gating network, and targeting to divide a complex problem into simple subtasks. An algorithm based on a maximum likelihood criterion is considered for the MOE network training. The results obtained with the proposed model show good generalization performance with nonlinear HPA and with memory effect in the presence of system imperfections.