Technology Category
Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsMachine Learning: Time-Series/Data StreamsSearch and Optimization: Mixed Discrete/Continuous Search
Spectrum Prediction in the Fractional Fourier Domain with Adaptive Filtering
🤖 AI Summary
Spectrum prediction in dynamic spectrum access (DSA) faces significant challenges due to strong noise interference and severe time-frequency domain feature entanglement. To address these issues, this paper proposes the Spectrum Forecasting via Fractional Fourier Domain (SFFP) framework—the first to incorporate adaptive fractional Fourier transform (AFrFT) into spectrum modeling. By learning optimal transform orders, SFFP achieves maximal signal-noise separation in the fractional domain. Furthermore, it integrates complex-valued neural networks with adaptive filtering to enable end-to-end feature enhancement and trend prediction directly within the fractional domain. Experimental evaluation on real-world spectrum datasets demonstrates that SFFP consistently outperforms state-of-the-art time-frequency domain methods, achieving 12.6%–23.4% improvements in prediction accuracy while exhibiting superior robustness under noisy and nonstationary conditions. This work establishes a novel paradigm for intelligent spectrum management in highly dynamic wireless environments.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Predicting spectrum accurately for dynamic access and resource allocation
Separating predictable patterns from noise in spectrum data
Enhancing trend-noise separability in fractional Fourier domain
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adaptive fractional Fourier transform for domain conversion
Adaptive filtering to suppress noise selectively
Complex-valued neural network for trend prediction
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