Explainable Spectrum Prediction Based on VMD-LSTM

Abstract

To improve the accuracy and interpretability of neural network enabled spectrum prediction, an explainable spectrum prediction framework based on Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) networks, integrated with the Shapley Additive Explanations (SHAP) method (VMD-LSTM), is proposed in this work. Firstly, the raw spectrum data is decomposed into multiple Intrinsic Mode Functions (IMFs) via VMD to reduce sequence complexity. These IMFs are then fed into the LSTM network in parallel to improve prediction accuracy. Secondly, the SHAP method is incorporated to evaluate the impact weights of individual IMF components on the prediction outcomes, revealing the model's decision-making logic. Finally, we weight the input data by multiplying each IMF by its SHAP value to optimize prediction performance. Simulation results based on real spectrum data demonstrate that the proposed VMD-LSTM significantly outperforms baseline models on the metrics of Weighted Quality Evaluation Index (WQE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and mean absolute error (MAE). By incorporating SHAP weights to refine the model input features, the framework not only provides transparent explanations for the black-box model but also reduces the average WQE, RMSE, and MAPE by 3.99%, 3.23%, and 3.67%, respectively.