The inherent nonlinear behavior exhibited by power amplifiers (PAs) in saturation mode is a major impediment in wireless systems to achieve higher power efficiency, and higher spectral efficiency. Although PA behavioral modeling and digital predistortion (DPD) techniques are widely utilized at transmitter to characterize and linearize such distorted nonlinear output of PAs, the nonlinear distortions with memory effects have become more severe due to simultaneous strong crosstalk between multiple PA branches in multiple-input multiple-output (MIMO) arrays. Furthermore, current multi-input behavioral models suffer from a sharp increase in number of coefficients as the number of transmitter path increases in MIMO. In overcoming these challenges, we propose a decomposed cross-correlation based single-input-single-output (CC-SISO) behavioral modeling architecture. The proposed solution utilizes a low-cost, novel cross-correlation based method to estimate and cancel the simultaneous nonlinear and reverse crosstalk from multiple PA branches. Once the crosstalk is mitigated, the MIMO DPD can be implemented with single-input DPD blocks which significantly reduces the complexity. Furthermore, CC-SISO DPD eliminates the requirement for signal feedback paths before and after the PA, and thus reduces overall hardware implementation complexity. Through simulations, we demonstrate that the proposed CC-SISO architecture can reduce the overall complexity of state-of-art multi-input DPD models for MIMO systems.

In this paper, an augmented crossover memory polynomial model (A-COMPM) is proposed and developed which can be used for characterizing and linearizing multiple-input multiple-output (MIMO) transmitters in the presence of linear and nonlinear crosstalk. The proposed model significantly improves the performance of the crossover memory polynomial model (CO-MPM) by more accurately incorporating the effect of crosstalk. The proposed model performs comparably to the 2 x 2 parallel Hammerstein (2 x 2 PH) model, while requiring the same number of coefficients as CO-MPM and a lower number of coefficients than the 2 x 2 PH. The model was tested for forward modeling and digital predistortion (DPD) applications in the presence of both linear and nonlinear crosstalk. Experimental results show that the model outperforms the CO-MPM and 2 x 2 PH DPDs, at a lower number of coefficients compared to both models. Furthermore, the issue of numerical stability of the DPD

This study addresses the ill‐conditioning problem of the memory polynomial (MP) model with application to the predistortion of highly non‐linear power amplifiers with memory effects. A resource‐efficient lattice‐based MP structure built using the cascade of a MP generator and a lattice predictor is proposed to overcome the ill‐conditioning of the MP's data matrix. The proposed model performances are benchmarked against those of the MP model as well as the orthogonal MP model. The experimental results demonstrate the suitability of the proposed predistorter as it achieves similar performance in the time and the frequency domains compared to the MP counterpart while alleviating its ill‐conditioning problem.