A compound structure and a single-step identification procedure for I/Q and DC offset impairments and nonlinear distortion modeling and compensation in wireless transmitters

The interplay between the unavoidable various nonlinearities of the direct conversion transmitter, such as local oscillator leakage, power amplifier (PA) nonlinearities, and in-phase and quadrature (I/Q) branch imbalance, and so forth will degrade the communication system performance seriously. To overcome these nonlinear interactive effects, an accurate adaptive sparse behavioral model is proposed for the joint compensation of the transmitter impairments in this article. First, a three-input nonlinear joint compensation model, which is composed of the nonlinear frequency-dependent cross terms between the I and Q branches as well as the magnitudes of the input signal, is developed.Second, to prune the redundant terms and reduce the computational complexity of the full three-input model, an efficient robust quasi-newton-based adaptive greedy algorithm is developed. To verify the performance of the proposed method, the different imperfect transmitters based on single-device GaN Class-F PA and GaN Doherty PA are used for experimental verification and analysis.Experiment results show that the proposed method can efficiently construct a sparse joint compensation model with improved modeling and distortion mitigation capability than the reported I/Q imbalance model, where nonlinear distortion and I/Q imbalance characteristics in the transmitter can be almost completely removed.

Section: Simulation and Experimental Resultsmentioning

confidence: 99%

“…Also, the new joint compensation model for memory PA and frequency‐dependent nonlinear IQ impairments was proposed in Refs. . However, these proposed models are only the simplified form of the dual‐input model proposed in Ref.…”

Section: Introductionmentioning

confidence: 99%

A simplified adaptive sparse digital pre‐distorter for joint mitigation of frequency‐dependent transmitter impairments

Yao

Jin

et al. 2019

“…Comparing rG p (z 1 ) in ( 20) and r 2 f(z 1 ) in ( 11), it can be observed obviously that (20) has a simpler form with (11), which means a simple calculation is needed at per iteration. Combining the above results, the Newton-Armijo algorithm is proposed to find the optimal upbound solution of the improved NSTSVR model.…”

Section: The Improved Nonlinear Smooth Tsvr Algorithmmentioning

confidence: 99%

“…Based on the conjugate polynomial based joint compensation approach, some similar approaches are studied extensively for the joint compensation of transmitter impairments. [10][11][12] However, there is an obvious disadvantage that the conjugate terms will increase the computational complexity of the compensation model. In Reference 13, the conjugate terms are removed, and the input is composed of the nonlinear frequency-dependent cross terms between the I and Q branches, and a three-input model joint compensator is proposed.…”

Section: Introductionmentioning

confidence: 99%

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

Cai

Yao

et al. 2021

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.

“…However, PA behaves nonlinearly in saturation region and produces distortions . This non‐linearity of the power amplifier can be overcome by digital predistortion (DPD) which is also the most popular technique for linearization of PA . The DPD comprises of nonlinear digital model before PA which pre‐distorts the incoming signal, which in turn cancels the distortion generated by PA. Several behavioral models consider static nonlinearities only but due to a poor decoupling in FET gate and drain as well as poor decoupling in BJT base and collector, electrical memory effects are present in PAs.…”

Section: Introductionmentioning

confidence: 99%

Application of principal component analysis based effective digital predistortion technique for low-cost FPGA implementation

Jaraut

Rawat

2017

This article investigates the issue of low-cost digital predistortion (DPD) implementation in fixed-point field programmable gate array (FPGA) by considering the bitresolution along with lower number of coefficients. The impact of principle component analysis (PCA) on bit-resolution of DPD solution is proposed within the context of established DPD models. Unlike previously proposed PCA based solutions, it is established by simulation and measurement that the numerical stability problem associated with popular models such as memory polynomial (MP) can be alleviated when PCA is applied to the observation data matrix. It is reported with measurement results that PCA based model provides better linearization performance with the least memory size requirement and number of LUTs in 16-bit fixed-point FPGA operation than MP, orthogonal memory polynomial (OMP), and generalized memory polynomial (GMP) models. The performance of the proposed model, is evaluated in terms of normalized mean square error, adjacent channel error power ratio, matrix condition number, and dispersion coefficient for continuous Class-AB and ZX60-V631 power amplifiers using wide code-division multiple access signal (WCDMA) and long term evolution (LTE) signal with peak-to-average-power ratio (PAPR) around 9.895 and 11.92 dB, respectively. K E Y W O R D S behavioral modeling, digital predistortion (DPD), memory effect, memory polynomial (MP), order reduction, power amplifier (PA), principal component analysis (PCA) Int J RF Microw Comput Aided Eng. 2017;27:e21095.