Interference Performance Evaluation Method Based on Transfer Learning and Parameter Optimization
A novel method for evaluating interference performance based on Transfer Learning(TL)and parameter optimization is proposed to address the limitation of single evaluation results obtained using traditional error rate assessment in digital communication systems.This method selects the core parameters of each signal processing module as the training index of machine learning and considers the evaluation results of the Technique for Order Preference by Similarity to the Ideal Solution(TOPSIS)as the classification standard.An SVM(Support Vector Machine)is used to train and evaluate the model.The parameter optimization problem in the SVM is addressed by enhancing the global search capability of Ant Colony Optimization(ACO).Moreover,the issue of missing data in the training samples is solved based on the knowledge transfer properties of TL.The results of the simulation experiments demonstrate that the SVM with access to the source domain dataset increases the model accuracy by 4.2%.Parameter optimization,which sacrifices the initial convergence ability,enhances the proximity to the optimal solution by 4.7%.In addition,it can be employed to evaluate the interference performance of digital communication systems.
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