Spectrum Prediction in the Fractional Fourier Domain with Adaptive Filtering
This addresses spectrum prediction for dynamic spectrum access and resource allocation, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of accurate spectrum prediction for dynamic spectrum access by proposing the SFFP framework, which uses adaptive fractional Fourier transforms and filtering to separate predictable trends from noise, and experiments show it outperforms leading methods.
Accurate spectrum prediction is crucial for dynamic spectrum access (DSA) and resource allocation. However, due to the unique characteristics of spectrum data, existing methods based on the time or frequency domain often struggle to separate predictable patterns from noise. To address this, we propose the Spectral Fractional Filtering and Prediction (SFFP) framework. SFFP first employs an adaptive fractional Fourier transform (FrFT) module to transform spectrum data into a suitable fractional Fourier domain, enhancing the separability of predictable trends from noise. Subsequently, an adaptive Filter module selectively suppresses noise while preserving critical predictive features within this domain. Finally, a prediction module, leveraging a complex-valued neural network, learns and forecasts these filtered trend components. Experiments on real-world spectrum data show that the SFFP outperforms leading spectrum and general forecasting methods.