A dual-loop parameter characterization structure is proposed in order to improve the accuracy of the model extraction in digital predistortion systems. In this concept, a model reference loop is used in conjunction with a model inverse structure for fine tuning the model parameters. This model extraction process does not increase much of the complexity of system implementation but experimental results show that linearization performance can be significantly improved by employing the proposed structure for wideband RF power amplifiers.Science Foundation Irelan
Abstract—This paper reports a new direct learning (DL) tech-nique using 2D quasi exact inverse (2D-Q...
This paper introduces a low-complexity stochastic optimization-based model coefficients extraction s...
Digital predistortion (DPD) using baseband signals is commonly used for power amplifier linearizatio...
A dual-loop parameter characterization structure is proposed in order to improve the accuracy of the...
Young Engineer Prize recipientInternational audienceDownload Citation Email Print Save to Project Th...
This brief presents a novel digital predistortion (DPD) parameter identification technique that requ...
Digital predistortion (DPD) using baseband signals is commonly used for power amplifier linearizatio...
2017 IEEE Topical Conference on RF/microwave Power Amplifiers for Radio and Wireless Applications (P...
To address the known trade-off between linearity and efficiency of the PA, several linearization tec...
A new behavioral model for digital predistortion of radio frequency (RF) power amplifiers (PAs) is p...
In this paper, a low-complexity model is proposed for linearizing power amplifiers with memory effec...
International audienceIn this paper, we propose a novel technique of digital predistortion (DPD) for...
This paper proposes a novel hardware implementation strategy to achieve low-cost design for digital ...
none4siWe present an algorithm for the real-time inversion of a two-input behavioral model applicabl...
Dynamic and flexible RF spectrum access through software-defined radio technologies is known to be l...
Abstract—This paper reports a new direct learning (DL) tech-nique using 2D quasi exact inverse (2D-Q...
This paper introduces a low-complexity stochastic optimization-based model coefficients extraction s...
Digital predistortion (DPD) using baseband signals is commonly used for power amplifier linearizatio...
A dual-loop parameter characterization structure is proposed in order to improve the accuracy of the...
Young Engineer Prize recipientInternational audienceDownload Citation Email Print Save to Project Th...
This brief presents a novel digital predistortion (DPD) parameter identification technique that requ...
Digital predistortion (DPD) using baseband signals is commonly used for power amplifier linearizatio...
2017 IEEE Topical Conference on RF/microwave Power Amplifiers for Radio and Wireless Applications (P...
To address the known trade-off between linearity and efficiency of the PA, several linearization tec...
A new behavioral model for digital predistortion of radio frequency (RF) power amplifiers (PAs) is p...
In this paper, a low-complexity model is proposed for linearizing power amplifiers with memory effec...
International audienceIn this paper, we propose a novel technique of digital predistortion (DPD) for...
This paper proposes a novel hardware implementation strategy to achieve low-cost design for digital ...
none4siWe present an algorithm for the real-time inversion of a two-input behavioral model applicabl...
Dynamic and flexible RF spectrum access through software-defined radio technologies is known to be l...
Abstract—This paper reports a new direct learning (DL) tech-nique using 2D quasi exact inverse (2D-Q...
This paper introduces a low-complexity stochastic optimization-based model coefficients extraction s...
Digital predistortion (DPD) using baseband signals is commonly used for power amplifier linearizatio...