Abstract
In this paper, a dynamic behavioral model for digital predistortion (DPD) of RF power amplifier (PA) based on amplitude and phase augmented time-delay twin support vector regression (AP-TSVR) is proposed. Unlike other SVR-based methods, the TSVR model finds a pair of non-parallel planes by solving two related support vector machine (SVM) type problems, namely, the $\varepsilon $ -insensitive up- and down-bound functions. Furthermore, in order to accelerate the training process, an effective linear regression algorithm was used to solve the paired quadratic programming problems (QPPs) of the TSVR model involved. The simulation results show that the proposed model is able to give improved modeling and distortion mitigation capability than the traditional memory polynomial-based model, and reduce CPU training time than the ordinary SVR model, even when the effects of both nonlinear characteristics and memory effects of PA are considered. To verify the effectiveness of the proposed method, experimental verification was performed using single-device gallium nitride (GaN) PA and GaN Doherty PA, respectively. The experimental results show that the new modeling approach can provide very efficient and extremely accurate linearization performance with improving generalization ability.
Highlights
radio frequency (RF) power amplifiers (PAs) are an integral part of wireless communication systems, especially in 4G and 5G mobile communications, where higher-order modulations are used to achieve higher spectrum utilization in limited spectrum resources
When the PA works in the saturation region, its nonlinear distortion will bring two consequences: one is spectrum regrowth, which will result in adjacent channel interference; and the other is the nonlinear distortion in the operation band, which will result in the increasing of the bit error rate of the communication system
It can be clearly seen from the figure that the "S" shape characteristic curves of Doherty PAs (DPAs) are close to the clear straight lines after the pre-distortion, which indicates that the nonlinear distortion and memory effect of the DPA have been successfully compensated by using the proposed AP-twin support vector regression (TSVR) model method in this paper
Summary
RF power amplifiers (PAs) are an integral part of wireless communication systems, especially in 4G and 5G mobile communications, where higher-order modulations are used to achieve higher spectrum utilization in limited spectrum resources. The SVR method owns better generalization ability compared with ANN method, the learning speed of classical SVR is very low, since it is constructed based on the minimization of a convex quadratic function which is subject to the pair groups of linear inequality constraints for all training samples. To overcome the issues of the traditional SVR method, a fast twin support vector regression (TSVR) behavioral modeling approach for DPD of PA was proposed in this paper. The learning speed of classical SVR model is slower, since the SVR model is constructed based on the minimization of a convex quadratic function (3), which is subject to the pair groups of linear inequality constraints for all training samples
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
