【正文】 而在現(xiàn)實(shí)中這是很難做到的。第四節(jié) 本章小結(jié)仿真結(jié)果表明，這種感知方法一方面在算法復(fù)雜度和感知性能方面得到了均衡。另一方面，該算法是一種盲檢測算法，無需知道主用戶的先驗(yàn)信息。因此，PCA感知算法完全彌補(bǔ)了單次感知算法的不足，具有較高的檢測精度，是一種快速精準(zhǔn)的盲頻譜感知算法。結(jié) 論頻譜感知對(duì)于實(shí)現(xiàn)認(rèn)知無線電至關(guān)重要，如何高效的進(jìn)行頻譜感知一直是研究的熱點(diǎn)。一方面，本文基于接收信號(hào)樣本協(xié)方差矩陣特征值理論和隨機(jī)矩陣?yán)碚?，研究了新的感知算法。同時(shí)，根據(jù)兩個(gè)熱門定理通過理論分析確立了虛警概率、判決門限以及檢測概率。該算法無需獲取相關(guān)信號(hào)、信道以及噪聲功率的先驗(yàn)信息，克服了能量檢測算法的噪聲不確定性問題。另一方面，基于仿真平臺(tái)對(duì)隨機(jī)生成相關(guān)信號(hào)和獨(dú)立信號(hào)進(jìn)行仿真，對(duì)相關(guān)信號(hào)的檢測，檢測算法的檢測性能最好，和檢測算法次之，檢測算法的檢測性能最差。對(duì)獨(dú)立信號(hào)的檢測，算法的檢測性能最佳，算法次之，和算法檢測性能最差。仿真得到隨著信噪比的增加，四種檢測算法的檢測性能都逐漸提高。與此同時(shí)，虛警概率幾乎不受信噪比的影響。對(duì)于一種檢測算法，當(dāng)其檢測概率越高，其檢測性能就越好，就能將和區(qū)分得更開。致 謝光陰似箭，一轉(zhuǎn)眼，半年光陰就要過去了，本文也即將付諸成稿。在論文工作即將結(jié)束走上工作崗位之際，回顧這半年難忘的時(shí)光，感既頗多，老師和其他同學(xué)給予了我太多的幫助。感謝我的導(dǎo)師劉占軍副教授，嚴(yán)謹(jǐn)?shù)目蒲凶黠L(fēng)和認(rèn)真的工作態(tài)度時(shí)時(shí)刻刻鞭策和激勵(lì)著我，大到論文的選題、數(shù)據(jù)處理的方式、實(shí)驗(yàn)方法的確定，小到實(shí)驗(yàn)設(shè)備的制作、報(bào)告措辭的推敲，都事無巨細(xì)地給我指導(dǎo)。不但一直以來在學(xué)習(xí)科研，而且在為人處事和生活方面給我引導(dǎo)和幫助。最后要感謝學(xué)校和學(xué)院給了我這次做科研的機(jī)會(huì)。謝謝您們！參考文獻(xiàn)[1] 李俊葶,陳金鷹,劉慶豐,[J].:7679.[2] 王東生，曹磊．混沌、分形及其應(yīng)用[M]．中國科學(xué)技術(shù)大學(xué)出版社，1995．[3] Hashler M,Mazzini G,lek M O,et al．Scanning the special issuespecial issue on applications of nonlinear dynamics to electronic and information engineering．Proceeding of the IEEE，2002，90(5):631～640.[4] National Bureau of Standards．FIPS PUB 46 Data Encryption Standards[S]．Washington DC：Federal Information Processing Standards，US Dept．Of Commerce，1997．[5] Fujisaka H and Yamada T． Stability theory of synchronized motion in coupledoscillator systems[J]．Porg TheorPhys，1983，69：32～47．[6] Pecora L M and Carroll T L．Synchronization in chaotic systems[J]．Phys Rev Lett，1990，64：821～823．[7] Abel A，Schwarz CommunicationsPrinciples，Schemes，and System Analysis．Proceedings of the IEEE，2002，90(5)：691～710．[8] Kolumban G，Kennedy M P，Kis G，et al．Performance Evaluation of FMDCSKModulation in Multipath Environments．IEEE Trans Circuits Syst．I，2000，47(12)：1702～1711．[9] Frey D R．Chaotic Digital Encoding：An Approach to Secure Communication．IEEE Trans Circuits Systs II，1993，40(10)：660～666．[10] Yang T，Chua L O．Secure Communication via Chaotic Parameter Modulation．IEEE Trans Circuits Systs I，1996，43(9)：817～819．[11] Kennedy M P，Dedieu demonstration of binary chaoticshiftkeying using selfsynchronising Chua39。s circuits．Presented at Proceedings of the International Workshop Nomlinear Dynamics in Electronic Systems，Singapore，1994，262～275．[12] 丁丹平，田立新．利用Lyapunov指數(shù)的混沌控制及控制參數(shù)選擇[J]．江蘇理工大學(xué)學(xué)報(bào)，2000，21(1)：87～91．[13] 蘭祝剛，彭巍，丘水生．混沌同步方法的研究[J]．通信技術(shù)，2000．1．[14] 翁貽方．混沌同步原理及其在保密通信中的應(yīng)用[J]．北京輕工業(yè)學(xué)院學(xué)報(bào)，2000．3．[15] 馬在光，吳純英，丘水生．混沌同步和混沌通信研究的新進(jìn)展與新嘗試[J]．電波科學(xué)學(xué)報(bào)，2002．6．[16] L．M．Pecora et al，Synchronization in Chaotic System，Phys，Rev，Lett，1990，Vol，64，No．8，821～824．[17] Huberman B A．Dynamics of adaptive systems．IEEE Trans Circuits Systs 1990，37：547～550．[18] Peterman D W et al．High frequency synchronization of chaos．Phys．Rev．Lett，1995，74(10)：1740～1742．[19] Rong He P．G．Vaidya．Physical Review A．46，7387(1992)．[20] [M].成都電子科技大學(xué)出版社,1999，321~325．[21] 王光瑞,于熙齡，陳式剛.《混沌的控制、同步與利用》[M].國防工業(yè)出版社.[22] L．O．Chua，The genesis of Chua’s circuit，Archiv fur Elektroik and Ubertragungstechnic Vol，46，No．4，1992，250～257．[23] 冉立新，陳抗生．蔡氏電路混沌同步的實(shí)驗(yàn)研究[J]．浙江大學(xué)學(xué)報(bào)(自然科學(xué)版)．1999．5．附 錄一、英文原文：A Novel TwoStage EntropyBased Robust Cooperative Spectrum Sensing Scheme with TwoBit Decisionin Cognitive RadioNan ZhaoSpringer Science+Business Media, LLC. 2012 Abstract Spectrum sensing is a key problem in cognitive radio. However, traditional detectors bee ineffective when noise uncertainty is severe. It is shown that the entropy of Gauss white noise is constant in the frequency domain, and a robust detector based on the entropy of spectrum amplitude was proposed. In this paper a novel detector is proposed based on the entropy of spectrum power density, and its performance is better than the previous scheme with less putational plexity. Furthermore, to improve the reliability of the detection, a twostage entropybased cooperative spectrum sensing scheme using twobit decision is proposed, and simulation results show its superior performance with relatively low putational plexity.Keywords Cognitive radio Cooperative spectrum sensing Information entropy Twostage detection Noise uncertaintyI. IntroductionOver the last decade, because of conflicts between the increasing demands of wireless munication services and the scarcity of wireless spectrum, cognitive radio (CR) networkrelated research has progressed rapidly [1]. In CR, the secondary users need to opportunistically sense the idle channels. Once an idle channel is sensed, the secondary users will access the channel. Hence, spectrum sensing requests the secondary users to efficiently and effectively detect the presence of the primary signals, and is a fundamental problem in CR [2]. Generally, spectrum sensing techniques can be classified into three categories, energy detection [3,4], matched filtering detection [5] and feature detection [6]. In the matched filtering detection and feature detection, the CR should have some knowledge about the primary signal features, such as preambles, pilots, synchronization symbols and modulation schemes. Hence, these two detection schemes require large putational costs and are not suitable to act as a blind detector. Energy detection is shown to be optimal if the cognitive devices do not have a priori information about the features of the primary signals, and it possesses the lowest putational costs and is easily implemented. However, it is susceptible to noise uncertainty and performs poorly at low SNR.Because of the fluctuation of background noise, noise uncertainty exists in every practical system. Sensitivity to noise uncertainty is a fundamental limitation of current spectrum sensing strategies in detecting the presence of the primary users (PU) in CR. Because of the noise uncertainty, the performance of traditional detectors deteriorates quickly when SNR is low [7]. The information entropy theory has been applied to the signal detection successfully, and thus several entropybased detectors have been prop