基于混沌時(shí)間序列建模的頻譜狀態(tài)持續(xù)時(shí)長(zhǎng)預(yù)測(cè)
doi: 10.11999/JEIT140959
基金項(xiàng)目:
國(guó)家自然科學(xué)基金青年科學(xué)基金(61201120)資助課題
Prediction of Spectrum State Duration Based on Chaotic Time Series Modelling
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摘要: 為提高頻譜利用率，該文利用非線性動(dòng)力學(xué)理論對(duì)頻譜狀態(tài)持續(xù)時(shí)長(zhǎng)序列進(jìn)行建模并預(yù)測(cè)。以實(shí)際采集的頻譜數(shù)據(jù)作為研究對(duì)象，采用指向?qū)?shù)法對(duì)該時(shí)長(zhǎng)序列進(jìn)行非一致延長(zhǎng)時(shí)間相空間重構(gòu)，利用基于尺度的Lyapunov指數(shù)判定其混沌特性。以基于Davidon-Fletcher-Powell方法的二階Volterra預(yù)測(cè)模型 (DFPSOVF)為基礎(chǔ)，提出一種基于限域擬牛頓方法的Volterra自適應(yīng)濾波器系數(shù)調(diào)整模型，并將該模型應(yīng)用于具有混沌特性的短時(shí)頻譜狀態(tài)持續(xù)時(shí)長(zhǎng)預(yù)測(cè)，通過(guò)自適應(yīng)剔除對(duì)預(yù)測(cè)貢獻(xiàn)小的濾波器系數(shù)，降低預(yù)測(cè)模型的復(fù)雜度。實(shí)驗(yàn)結(jié)果表明該算法在保證預(yù)測(cè)精度的同時(shí)降低運(yùn)算復(fù)雜度。
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關(guān)鍵詞:
- 頻譜感知 /
- 頻譜預(yù)測(cè) /
- 混沌 /
- 限域擬牛頓方法
Abstract: In order to enhance the spectrum utilization, this paper uses the nonlinear dynamics theory for modeling and prediction of spectrum state duration. Firstly, the real spectrum state duration is investigated. Then, this study uses the directional derivative to accomplish the state-space reconstruction of the spectrum time series with the non-uniform time delays. Finally, the Scale-Dependent Lyapunov Exponent (SDLE) is used to determine the characteristics of chaos. Based on the Davidon-Fletcher-Powell-based Second Order of Volterra Filter (DFPSOVF) method, a novel Volterra model with adaptive coefficient adjusting using Limited storage Broyden-Fletcher- Goldfarb-Shanno quasi-Newton (L-BFGS) method is proposed. Furthermore, the proposed model is applied to predict the short-term spectrum with chaotic characteristics. To reduce the complexity of this new model, the useless filter coefficients are eliminated adaptively. The numerical simulations show that the new algorithm can reduce the complexity and guarantee prediction accuracy.-
Key words:
- Spectrum sensing /
- Spectrum prediction /
- Chaos /
- Limited storage quasi-Newton method
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