發(fā)布時(shí)間：2018-05-19 13:28

  本文選題：認(rèn)知無線電 + 認(rèn)知無線傳感器網(wǎng)絡(luò)��； 參考：《南京郵電大學(xué)》2017年碩士論文

【摘要】：無線傳感器網(wǎng)絡(luò)(Wireless Sensor Networks,WSN)因應(yīng)用廣泛而備受研究人員關(guān)注,但是頻譜資源緊缺問題限制了它的發(fā)展。將認(rèn)知無線電(Cognitive Radio,CR)技術(shù)應(yīng)用到無線傳感器網(wǎng)絡(luò)中組成認(rèn)知無線傳感器網(wǎng)絡(luò)(Cognitive Radio Sensor Networks,CRSN)為解決這一問題提供了方法。為了在不干擾主用戶(Primary User,PU)的正常通信的前提下接入授權(quán)頻譜,認(rèn)知無線傳感器網(wǎng)絡(luò)的認(rèn)知節(jié)點(diǎn)需要不斷地檢測(cè)周圍的頻譜資源是否被主用戶占用。因此,頻譜感知技術(shù)不僅是認(rèn)知無線電技術(shù)的重要基礎(chǔ),也是區(qū)分認(rèn)知無線傳感器網(wǎng)絡(luò)與無線傳感器網(wǎng)絡(luò)的重要功能。然而,認(rèn)知無線傳感器網(wǎng)絡(luò)中的頻譜感知檢測(cè)技術(shù)研究仍然面臨著一些挑戰(zhàn):首先,現(xiàn)有的采用壓縮感知(Compressive Sensing,CS)理論的算法大都需要重構(gòu)原信號(hào),算法計(jì)算復(fù)雜度很高;其次,傳統(tǒng)的能量檢測(cè)(Conventional Energy Detection,CED)頻譜感知方法需要大量的采樣樣本數(shù)并且在低信噪比時(shí)檢測(cè)性能容易因噪聲波動(dòng)而產(chǎn)生影響。為了解決以上問題,本文提出了兩種頻譜檢測(cè)算法:(1)提出一種基于高階統(tǒng)計(jì)量(High-Order Statistic,HOS)的壓縮寬帶頻譜盲檢測(cè)算法(簡(jiǎn)稱HOS-CWSBD),該算法利用了壓縮觀測(cè)數(shù)據(jù)使采樣數(shù)據(jù)量大大減少;并采用高階統(tǒng)計(jì)量作為頻譜檢測(cè)的判決量,不需要重構(gòu)出原信號(hào),計(jì)算復(fù)雜度降低,在不知道主用戶先驗(yàn)知識(shí)的情況下,也有良好的檢測(cè)性能。(2)提出一種小樣本能量檢測(cè)中的雙門限協(xié)作頻譜感知方法,該算法不需要知道主用戶的先驗(yàn)知識(shí),并采用雙門限減少了低信噪比時(shí)認(rèn)知用戶對(duì)主用戶的干擾,利用多維高斯(Cubeof-Gaussian,CoG)近似處理檢測(cè)結(jié)果,克服了傳統(tǒng)能量檢測(cè)方法因需要足夠大的樣本數(shù)使得傳輸數(shù)據(jù)大而導(dǎo)致認(rèn)知無線傳感器網(wǎng)絡(luò)的節(jié)點(diǎn)能量消耗過大的問題,在融合中心(Fusion Center,FC)使用“大多數(shù)投票”原則做出最終判決,提高整個(gè)系統(tǒng)的檢測(cè)性能。
[Abstract]:Wireless Sensor Networks (WSNs) has attracted the attention of researchers because of its wide application, but its development is limited by the shortage of spectrum resources. The Cognitive Radio Sensor Networks (CRSNs) are applied to the cognitive wireless sensor networks (WSN) to solve this problem. In order to access the authorized spectrum without interfering with the primary user's normal communication, cognitive nodes of cognitive wireless sensor networks need to constantly detect whether the spectrum resources around them are occupied by the primary users. Therefore, spectrum sensing technology is not only an important basis of cognitive radio technology, but also an important function to distinguish cognitive wireless sensor networks from wireless sensor networks. However, the research of spectrum sensing detection in cognitive wireless sensor networks still faces some challenges: first, most of the existing algorithms based on compressed sensing theory need to reconstruct the original signal, and the computational complexity of the algorithm is very high. The conventional Energy detector (CE) spectrum sensing method requires a large number of samples, and the detection performance is easily affected by noise fluctuation at low signal-to-noise ratio (SNR). In order to solve the above problems, this paper proposes two spectrum detection algorithms: 1) A blind detection algorithm for compressed broadband spectrum based on high-order statistics (HOS-CWSBDN), which uses compressed observation data to greatly reduce the sample data. Using high-order statistics as the decision quantity of spectrum detection, it is not necessary to reconstruct the original signal, and the computational complexity is reduced, without knowing the priori knowledge of the primary user. There is also a good detection performance. 2) A dual threshold cooperative spectrum sensing method for small sample energy detection is proposed. The algorithm does not need to know the prior knowledge of the primary user. The dual threshold is used to reduce the interference of cognitive users to the primary users at low SNR, and the multi-dimensional Gao Si is used to approximate the detection results of Cubeof-GaussianCoG. It overcomes the problem that the energy consumption of nodes in cognitive wireless sensor networks is too large due to the large number of samples needed in the traditional energy detection methods. The final decision is made using the "majority vote" principle in fusion center FCs to improve the detection performance of the whole system.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN925;TP212.9

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本文編號(hào)：1910279