通过分数傅里叶域自适应滤波，提升频谱预测精度。
Spectrum Prediction in the Fractional Fourier Domain with Adaptive Filtering
- 在分数傅里叶域进行自适应变换，增强可预测趋势与噪声的分离度。
- 在真实数据上，相比主流方法，预测误差降低12.3%以上。
- 适合需要高精度频谱预测的动态频谱接入场景。
精准的频谱预测对动态频谱接入（DSA）和资源分配至关重要。然而，由于频谱数据的特殊性，基于时域或频域的传统方法常难以区分可预测模式与噪声。为此，本文提出谱分数滤波与预测（SFFP）框架。SFFP首先利用自适应分数傅里叶变换（FrFT）模块，将频谱数据映射至合适的分数傅里叶域，提升可预测趋势与噪声的可分性；随后，自适应滤波模块在该域内选择性抑制噪声，同时保留关键预测特征；最后，基于复值神经网络的预测模块学习并预测这些滤波后的趋势成分。在真实频谱数据上的实验表明，SFFP显著优于领先的频谱预测与通用预测方法。
原文摘要 · Abstract (English)
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.
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