Dual-loop Model Extraction for Digital Predistortion of Wideband RF Power Amplifiers

Preparing Linearity and Efficiency for 5G: Digital Predistortion for Dual-Band Doherty Power Amplifiers with Mixed-Mode Carrier Aggregation

“…In this way, the optimal DPD characteristic can be achieved for a specific DPD model, leaving only random noise and measurement errors. The cost of this improved accuracy is the greater number of iterations required to converge and the risk that the adaptation may be unstable if there is a big difference between the original PA input and observed output [19].…”

“…Based on the analysis of strengths and defects for each structure, a dual-loop strategy was proposed to overcome disadvantages in both IDLA and DLA, constructing a more accurate and stable model extraction [19]. Pictured in Figure 5, the dual-loop extraction procedure uses the indirect learning architecture for the initial coarse coefficient extraction and then switches to the direct learning architecture for a number of iterations to fine tune the coefficients.…”

Low-Complexity Stochastic Optimization-Based Model Extraction for Digital Predistortion of RF Power Amplifiers

“…In contrast, outlined in Fig. 6, the closed-loop approach places the DPD model inside the estimation loop and iteratively updates the coefficients using a separate error model [18], [19] (17) and is the adaptation factor. The closed-loop converges slower but it can typically achieve greater accuracy when it reaches the steady state.…”

Green Communications: Digital Predistortion for Wideband RF Power Amplifiers

“…Another optimized dual-loop model parameter scheme was also proposed for enhancing the single-loop parameter extraction performance at a cost of slightly increasing the complexity [92]. Table VI summaries the main contributions in the litera- [28], [30], [91] because of their well-known performance in estimating the coefficients of linear-in parameter models.…”

“…As the signal bandwidth increases, extracting the DPD parameters using reduced bandwidth becomes a challenging problem. Several helpful approaches can be used to efficiently derive DPD parameters in this band-limited situation, such as using reduced memory correction approach [95], direct learning with reduced bandwidth feedback [94], bandwidth-constrained least squares [96] and dual-loop parameter optimization [92].…”