A Time-Domain Multi-Tone Distortion Model for Effective Design of High Power Amplifiers

Abstract

This paper proposes a new time-domain multi-tone distortion (TD-MTD) model suitable for accurately predicting the non-linear behavior of packaged high power radio frequency (RF) transistors over a range of discrete non-uniformly distributed frequencies. This proposed TD-MTD model uses a single expression rather than multiple distinct frequency specific behavioral models to describe the underlying behavior of the high power RF transistor at multiple fundamental frequencies. Furthermore its extraction is carried out using a time-domain representation of the travelling waves that can be acquired using a generic vector load-pull characterization system and without imposing additional requirements. The proposed model is extracted as an artificial neural network (ANN) and is implemented as a Netlist to serve in a harmonic balance simulator based power amplifier design process. The proposed model is validated in two phases. First, its ability to reproduce the large-signal behavior of a high power RF LDMOS transistor was demonstrated in simulation. Then, the TD-MTD model was used to validate the design of a high power two-way asymmetric Doherty power amplifier and the simulated output-power-dependent power efficiency, AM/AM, AM/PM and input return loss characteristics were compared to those obtained in measurement. The excellent agreement between the simulation and measurement results confirms the usefulness of the proposed model despite the simplicity of its extraction routine and measurement data.INDEX TERMS artificial neural network, behavioral model, computer-aided design, harmonic balance simulation, load-pull, measurement-based design, non-linear time-invariant model, poly-harmonic distortion model, power amplifier design, power transistor model, Volterra series

Mobile Music Recognition based on Deep Neural Network

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“…is approach successfully detects the music signal's core fundamental frequency. By building pitch models for music data and comparing the weights of each pitch model, [14] achieves fundamental frequency detection.…”