Generative Augmented Cascade Broad Learning for Lightweight Multi-Band Spectrum Prediction

The rapid development of wireless communication and Internet of Things (IoT) devices has exacerbated the challenge of spectrum scarcity. Spectrum prediction plays a critical role in enhancing resource utilization and facilitating dynamic spectrum access (DSA). However, existing methods often face difficulties with missing data arising from sensor failures or environmental changes, while prioritizing prediction accuracy over computational efficiency, hindering deployment. To overcome these challenges, this paper introduces the generative augmented cascade broad learning network (GA-CBLN) for spectrum prediction. This approach combines a deep learning (DL)-based pre-trained model for data imputation with an online broad learning (BL) process to ensure efficient prediction. Initially, a DL-based regressor imputes missing values by learning spatiotemporal patterns, generating a complete dataset. Subsequently, a generative adversarial network (GAN) produces synthetic samples to augment the dataset, enhancing the model’s adaptability and generalization. Finally, the cascade broad learning network (CBLN) conducts feature extraction and prediction using both original and augmented data, exploiting the efficient learning capabilities of BL. Simulations on real spectrum datasets from four widely used frequency bands show that GA-CBLN surpasses traditional methods in computational efficiency, maintaining robust performance even under high rates of missing data.