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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2508.17872 |
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| _version_ | 1866911120643588096 |
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| author | Qin, Yanghao Zhou, Bo Pan, Guangliang Wu, Qihui Tao, Meixia |
| author_facet | Qin, Yanghao Zhou, Bo Pan, Guangliang Wu, Qihui Tao, Meixia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17872 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spectrum Prediction in the Fractional Fourier Domain with Adaptive Filtering Qin, Yanghao Zhou, Bo Pan, Guangliang Wu, Qihui Tao, Meixia Machine Learning Information Theory 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. |
| title | Spectrum Prediction in the Fractional Fourier Domain with Adaptive Filtering |
| topic | Machine Learning Information Theory |
| url | https://arxiv.org/abs/2508.17872 |