GA-CBLN: A Generative Augmented Cascade Broad Learning Network for Efficient Spectrum Prediction with Missing Data Handling
Niancong Ji, Ziqin Feng, Shufei Wang, Yibin Zhang, Tomoaki Otsuki, Guan Gui, Hikmet Sari · 2025
With the advancements in wireless communication and the growth of Internet of Things (IoT), spectrum scarcity has become a key bottleneck. Spectrum prediction enhances resource utilization and supports dynamic spectrum access (DSA). However, current models struggle with missing data and prioritize accuracy over efficiency, limiting practical use. To address this, we propose a generative augmented cascade broad learning network (GA-CBLN) for spectrum prediction, combining a deep learning (DL) pre-trained model with an online broad learning (BL) process. First, a DL-based regressor fills missing values by learning spatiotemporal patterns. Next, a generative adversarial network (GAN) generates synthetic samples to enrich the training set, improving adaptability. Finally, the cascade BL network (CBLN) uses the combined data for efficient prediction. Simulations on real datasets show that GA-CBLN significantly outperforms traditional methods in accuracy and efficiency, even with high missing data rates.