研究目的
To develop a dynamic behavioral modeling technique for RF power amplifiers using time-delay support vector regression to account for nonlinearity and memory effects, improving accuracy and efficiency over traditional methods.
研究成果
The proposed time-delay SVR model provides efficient and accurate behavioral modeling for RF PAs with nonlinearity and memory effects, outperforming traditional models like Volterra, CPWL, and ANN-based approaches. It shows good extrapolation capabilities and can be optimized quickly using grid search.
研究不足
The model may require further optimization for extrapolation across wider power ranges and could be computationally intensive during grid search. Trapping effects in GaN devices increase modeling complexity.
1：Experimental Design and Method Selection:
The methodology involves using a time-delay SVR approach for behavioral modeling of RF PAs, incorporating machine learning principles. The grid-search technique is employed for hyperparameter optimization.
2：Sample Selection and Data Sources:
Input-output data from three types of PAs (LDMOS PA, single GaN PA, Doherty GaN PA) driven by WCDMA and LTE signals are used for training and validation.
3：List of Experimental Equipment and Materials:
PAs include an in-house designed LDMOS PA, a GaN transistor (CGH40010F from Wolfspeed), and an in-house designed GaN Doherty PA. Test bench setup as in [14], with sampling rates of
4：64 MS/s and 400 MS/s. Experimental Procedures and Operational Workflow:
3 Data collection involves measuring input-output samples, time alignment, normalization, and using R programming for model training and validation. Grid search is applied to optimize hyperparameters (ε, C, γ).
5：Data Analysis Methods:
Performance is evaluated using root-mean-squared error (RMSE) and normalized mean square error (NMSE) metrics.
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