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Dynamic behavioral modeling of 3G power amplifiers using real-valued time-delay neural networks | IEEE Journals & Magazine | IEEE Xplore

Dynamic behavioral modeling of 3G power amplifiers using real-valued time-delay neural networks


Abstract:

In this paper, we propose a novel real-valued time-delay neural network (RVTDNN) suitable for dynamic modeling of the baseband nonlinear behaviors of third-generation (3G...Show More

Abstract:

In this paper, we propose a novel real-valued time-delay neural network (RVTDNN) suitable for dynamic modeling of the baseband nonlinear behaviors of third-generation (3G) base-station power amplifiers (PA). Parameters (weights and biases) of the proposed model are identified using the back-propagation algorithm, which is applied to the input and output waveforms of the PA recorded under real operation conditions. Time- and frequency-domain simulation of a 90-W LDMOS PA output using this novel neural-network model exhibit a good agreement between the RVTDNN behavioral model's predicted results and measured ones along with a good generality. Moreover, dynamic AM/AM and AM/PM characteristics obtained using the proposed model demonstrated that the RVTDNN can track and account for the memory effects of the PAs well. These characteristics also point out that the small-signal response of the LDMOS PA is more affected by the memory effects than the PAs large-signal response when it is driven by 3G signals. This RVTDNN model requires a significantly reduced complexity and shorter processing time in the analysis and training procedures, when driven with complex modulated and highly varying envelope signals such as 3G signals, than previously published neural-network-based PA models.
Published in: IEEE Transactions on Microwave Theory and Techniques ( Volume: 52, Issue: 3, March 2004)
Page(s): 1025 - 1033
Date of Publication: 31 March 2004

ISSN Information:


I. Introduction

System-Level behavioral modeling consists of constructing a black-box analytic function that admits alike responses to those obtained at the output of a real device or a subsystem driven by the same input signal. Such models are of great help for designers of communication systems, in particular, transmitters and power amplifiers (PAs) since they provide them with greatly reduced complexity and time-consuming design and optimization procedures. However, these advantages could be achieved providing the capacity of the model to predict the PA output under realistic conditions.

References

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