本文選題：認(rèn)知無線電　切入點(diǎn)：頻譜預(yù)測　出處：《西安電子科技大學(xué)》2014年碩士論文

【摘要】：頻譜預(yù)測是認(rèn)知無線電中的一項關(guān)鍵技術(shù),它是指認(rèn)知系統(tǒng)根據(jù)獲得的信道歷史信息,分析頻譜的使用規(guī)律,進(jìn)而預(yù)測頻譜的空洞信息,以指導(dǎo)認(rèn)知設(shè)備進(jìn)行智能的頻譜感知和動態(tài)的頻譜接入,從而降低主次用戶的碰撞率,提高認(rèn)知系統(tǒng)的整體性能,最終達(dá)到提高頻譜利用率的目的。由于現(xiàn)有頻譜預(yù)測算法復(fù)雜度高、準(zhǔn)確度低、所需歷史序列長,不適用于能量受限的認(rèn)知節(jié)點(diǎn)和快變信道環(huán)境。本文以提高預(yù)測準(zhǔn)確度降低預(yù)測復(fù)雜度為目的,提出一種基于上下文樹的可變長Markov預(yù)測方法。該方法將認(rèn)知用戶感知的歷史狀態(tài)信息構(gòu)建成可變階的狀態(tài)樹,在進(jìn)行預(yù)測時動態(tài)改變所需的最近歷史狀態(tài)數(shù)目,具有復(fù)雜度低、預(yù)測準(zhǔn)確度高、所需歷史序列短的特點(diǎn),并能通過周期的更新狀態(tài)樹提高非平穩(wěn)環(huán)境下的預(yù)測準(zhǔn)確度�？紤]到頻譜檢測誤差對預(yù)測準(zhǔn)確度的影響,進(jìn)一步完善上述算法,提出一種隱馬爾科夫模型與上下文樹可變長Markov模型相結(jié)合的頻譜預(yù)測方法,先是訓(xùn)練隱馬爾科夫模型恢復(fù)真實(shí)的信道狀態(tài)序列,然后在此基礎(chǔ)上構(gòu)建狀態(tài)樹,從而取消頻譜檢測誤差對預(yù)測性能的影響。利用基于排隊模型產(chǎn)生的頻譜數(shù)據(jù),在平穩(wěn)環(huán)境和非平穩(wěn)環(huán)境下分別驗證了基于上下文樹可變長Markov方法的有效性。又分別利用排隊模型和離散時間Markov模型產(chǎn)生的頻譜使用數(shù)據(jù),驗證了在檢測誤差存在的情況下,隱馬爾科夫模型與上下文樹可變長Markov模型相結(jié)合的算法的有效性。
[Abstract]:Spectrum prediction is a key technology in cognitive radio, it refers to the cognitive system according to the channel history information, analysis of use of spectrum, then forecast the cavity spectrum, spectrum sensing and dynamic spectrum access to intelligence to guide the cognitive devices, thereby reducing the collision rate of the primary and secondary users, improve the overall performance of cognitive the system, finally achieve the purpose of improving spectrum efficiency. Because the existing spectrum prediction algorithm with high complexity and low accuracy, the history of long sequences, cognitive nodes do not apply to the limited energy and fast varying channel environment. In order to improve the prediction accuracy for the purpose of reducing prediction complexity, proposes a prediction method variable length Markov tree based on context. This method will be the cognition history state information users' perception of the constructed tree state variable order, dynamic changes in forecasting required recently The number of the history of the state, with low complexity and high prediction accuracy, the characteristics of historical short sequences, and can improve the forecasting accuracy of stable environment by updating the state tree cycle. Considering the spectrum detection error to predict the accuracy, to further improve the algorithm, proposed a combination of hidden Markov spectrum model and context tree variable length Markov model prediction method, first channel state Cin Markoff model to restore the real training sequence, then established the state tree, thus eliminating influence of detection error on the performance of the pre measured spectrum. Using the spectral data generated based on queuing model, in a stable environment and non-stationary environment respectively the effectiveness of the context variable length tree based on Markov method were used. Spectrum queuing model and discrete time Markov model using data validation In the case of detection error, the effectiveness of the algorithm combining the hidden Markov model with the variable length Markov model of the context tree is effective.

【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN925

【參考文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 陳斌華;認(rèn)知無線電系統(tǒng)中的頻譜預(yù)測算法研究[D];北京郵電大學(xué);2011年

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本文編號：1695990