An improved nonlinear smooth twin support vector regression based‐behavioral model for joint compensation of frequency‐dependent transmitter nonlinearities

Abstract

In this article, an improved nonlinear smooth twin support vector regression (NSTSVR) model is proposed for the modeling and compensating of the transmitter nonlinearities jointly. The proposed model is an improved version of the twin support vector regression (TSVR) model by introducing a smooth function to replace the loss function of TSVR, which can change the dual space solution to the original space solution and speed up the solving solution. In addition, in order to solve the problem of long training time for large sample data in traditional SVR or TSVR model, the new algorithm further adopts the model pruning techniques, such as deleting the kernel matrix and finding sparse diagonal matrices, to reduce the size of the Hessian matrix in the fast Newton iteration process. To verify the performance of the proposed model, two transmitters based on single-device gallium nitride (GaN) PA with IQ imbalance and GaN Doherty PA with modulator imperfections are used for experimental verification and analysis. The experimental results show that the proposed model is superior to the conventional support vector regression and TSVR machine learning models in terms of modeling effect and linearization ability. Furthermore, the proposed model can achieve the improved compensation performance for transmitter impairments compared with some popular Volterra series-based I/Q imbalance models.

Quantitative Evaluation of Piano Performance Technique and Style Based on Continuous Discretization Method

“…When the training data set is not linearly correlated using the above model will result in a large prediction error, then a nonlinear support vector regression is required [17].…”

“…4) Calculate the decimal rhythm evaluation score (rhy) based on the rhythmic correctness of the whole score using equation (17), where 1[]  means rounding to retain 1 decimal place.…”

“…Dandan Mao and Sha Liu. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] …”

Predistortion-Based Linearization for 5G and Beyond Millimeter-Wave Transceiver Systems: A Comprehensive Survey

“…The fundamental methodology of the basic SVRbased PA model is to use all the present/past polynomial amplitude and phase-modulated time-delay SVR signals to convey real and imaginary parts to the SVR trainers for building the PA behavioral model. Then, the corresponding kernel functions comprised of a large number of training samples in basic or time-delay SVR models are accomplished to realize a DPD mechanism [125], [128], [129]. According to [125], the SVR nonlinear function f(x) for PA modeling can be modeled as:…”

“…Then, to account for the PA memory effects, the transmitted signals need to have the present and the history polynomials of phase and amplitude terms of an input signal. The works in [128] and [129] have also included the higher-order exponential terms of phase and amplitude characteristics of the input This article has been accepted for publication in IEEE Communications Surveys & Tutorials. This is the author's version which has not been fully edited and content may change prior to final publication.…”