發(fā)布時間：2018-06-14 07:43

  本文選題：認知無線電 + 壓縮感知��； 參考：《北京郵電大學(xué)》2014年博士論文

【摘要】：全球無線通信技術(shù)的發(fā)展日新月異,最有代表性的是蜂窩移動通信系統(tǒng)、無線局域網(wǎng)、衛(wèi)星通信、短距離無線通信及集群通信等。在目前固定的頻譜分配方式下,使得能用于再分配和使用的頻率變成了極為稀缺的資源,同時,這些已向授權(quán)用戶分配的頻譜使用效率并不高。因此,如何提高頻譜使用效率現(xiàn)已成為熱點研究課題。認知無線電系統(tǒng)作為一個智能無線通信系統(tǒng),在確保主用戶在其授權(quán)頻段上具有優(yōu)先使用權(quán),并且感知用戶的接入不影響主用戶性能的前提下,感知用戶設(shè)備能通過自主的調(diào)整,在當前未使用的空閑頻段上通信,提高頻譜的使用效率。認知無線電的概念一經(jīng)提出,國內(nèi)外的許多學(xué)者就表現(xiàn)出了極大的興趣,對其展開了廣泛而深入的研究,并且獲得了許多研究成果。主要包括頻譜檢測、認知引擎、動態(tài)頻譜管理、端到端重配置等方面。其中,用于感知信道占用狀態(tài)的頻譜檢測技術(shù)是認知無線電系統(tǒng)中非常重要的問題之一。 頻譜檢測的目的有兩個：一是感知用戶通過切換到其他可用頻段或者將自己對授權(quán)用戶的干擾限制到一個可以接受的范圍從而避免對授權(quán)用戶造成干擾；二是,感知用戶應(yīng)該有效利用頻譜空洞去滿足系統(tǒng)性能要求。因此,認知無線電中的頻譜感知準確度對于主用戶和次用戶的性能有至關(guān)重要的影響。 國內(nèi)外學(xué)者們從頻譜能量、頻譜特征、協(xié)同檢測等方面對頻譜檢測展開研究,提出了能量檢測、匹配濾波檢測、循環(huán)特征檢測、協(xié)作頻譜檢測等算法,顯著提高頻譜檢測的精度,而且還可以減小單個認知節(jié)點的負擔。然而,當前無線通信技術(shù)向移動、寬帶、高速的方向不斷發(fā)展,在未來的寬帶無線通信系統(tǒng)中要支持Gb／s甚至數(shù)Gb／s的分組數(shù)據(jù)傳輸速率,這使得系統(tǒng)帶寬不斷增加,同時,也要求極其靈活的頻譜資源分配。這就對頻譜檢測技術(shù)提出了新的挑戰(zhàn)：需要在寬頻帶范圍內(nèi)對頻譜進行快速、有效和動態(tài)的檢測。因此研究認知無線電系統(tǒng)中的寬帶頻譜檢測技術(shù),提高頻譜利用率,以適應(yīng)未來高速率移動無線通系統(tǒng)是非常必要的。目前國內(nèi)外研究人員針對寬帶頻譜檢測提出了一些研究方法,主要包括本地單節(jié)點多窄帶并行鏈路檢測和單節(jié)點寬頻帶檢測兩種類型。單節(jié)點寬頻帶檢測中,每個節(jié)點只需要一個射頻前端,通過模擬數(shù)字轉(zhuǎn)化器(Analog to Digital Converter, ADC)可以靈活地檢測功率譜密度動態(tài)變化的寬帶信號。然而,在寬頻帶范圍內(nèi),根據(jù)奈奎斯特采樣定律,頻譜檢測很難跨越超高速ADC芯片和海量存儲技術(shù)的壁壘,現(xiàn)有的A／D器件和存儲芯片難以勝任。在全頻段內(nèi),如何對接收到的多個目標信號進行檢測和判決,己經(jīng)成為制約寬帶頻譜檢測的一個難題。 壓縮感知理論為數(shù)據(jù)采集技術(shù)帶來了革命性的突破,將壓縮感知理論與寬帶頻譜檢測技術(shù)相結(jié)合,可以解決ADC硬件采樣頻率不足的問題�；凇皦嚎s感知”的寬帶頻譜檢測技術(shù)是一種新型信號處理技術(shù),其在理論上給出了可以利用頻譜信號的稀疏性進行寬頻帶范圍檢測,但由于其處于研究起步階段,大多數(shù)研究者直接把壓縮感知理論套用到認知無線電中的寬帶頻譜檢測,而經(jīng)過壓縮采樣后,后端恢復(fù)算法的復(fù)雜度非常高,在實際檢測中帶來了嚴重的時延問題。 針對寬帶認知無線電系統(tǒng)中頻譜環(huán)境動態(tài)、異構(gòu)和寬頻帶等特點,論文以壓縮感知理論為切入點,通過明確模擬信息轉(zhuǎn)換器(AIC)與寬帶異構(gòu)頻譜的內(nèi)在關(guān)系,分析認知無線電中對寬帶頻譜信號檢測的需求特點,利用源信號稀疏模型與信號先驗信息的內(nèi)在耦合機理,引入塊稀疏信號壓縮感知的理論,建立寬帶認知無線電系統(tǒng)中的頻譜動態(tài)檢測模型,在主用戶網(wǎng)絡(luò)干擾容限約束條件下,研究并設(shè)計出一套適用于當前認知無線電架構(gòu)的、符合特定信號頻譜特征的寬帶頻譜檢測策略。使得認知無線電技術(shù)更好地適用于未來的無線通信系統(tǒng),最大限度地利用頻譜資源,為認知無線電技術(shù)的進一步應(yīng)用提供可選的解決方案。 本文的主要創(chuàng)新和貢獻如下： 1.提出一種基于壓縮感知的非稀疏信號導(dǎo)頻檢測方法,該方法是基于壓縮域中的譜估計算法,直接使用壓縮感知得到的觀測值進行頻譜檢測,從而降低數(shù)據(jù)量和算法復(fù)雜度,減小檢測延時,以解決認知節(jié)點有限的計算能力與壓縮感知恢復(fù)算法較高的計算復(fù)雜度之間的矛盾。 該方法在壓縮感知理論的框架下,以主用戶的頻譜劃分為先驗信息,設(shè)計與劃分結(jié)構(gòu)相匹配的導(dǎo)頻圖案,在不恢復(fù)采樣信號的前提下,利用數(shù)字傅里葉變換的線性運算性質(zhì),對采樣信號在壓縮域進行線性運算,只保留頻譜空洞處導(dǎo)頻的頻譜信息,通過后端信號處理模型,直接從壓縮采樣的觀測值中估計非稀疏信號的參數(shù)如載波頻率、帶寬、功率等。通過設(shè)計仿真,驗證了所提出的導(dǎo)頻檢測方法,證明其能夠提高壓縮感知理論對非稀疏信號檢測準確性,達到減小重構(gòu)復(fù)雜度、增強接收機對信號類型的魯棒性的目的。 2.提出一種壓縮感知框架下的源信號頻域采樣結(jié)構(gòu),它是一種基于頻域采樣理論的信號采樣方法,通過在采樣過程中加入變換基矩陣,來充分利用主用戶信號在變換域中的稀疏特性,以解決現(xiàn)有壓縮感知采樣結(jié)構(gòu)中沒有變換基的問題,提高對頻域稀疏信號的檢測準確性。 該方法通過對現(xiàn)有采樣結(jié)構(gòu)的研究,設(shè)計了一種帶有變換基矩陣的頻域隨機解調(diào)器(FRD),該結(jié)構(gòu)通過多路并行通道,用隨機化的不同階頻域采樣信號對原始信號進行預(yù)處理,使得對信號的采樣可以在頻域進行,將處理結(jié)果通過積分器后分別進行低速抽樣判決,以獲得對模擬信號的壓縮測量值。通過設(shè)計仿真,與現(xiàn)有采樣器進行對比,所提出的FRD采樣結(jié)構(gòu)在現(xiàn)有硬件設(shè)備的條件下,能夠?qū)π盘栠M行頻域采樣,提高檢測頻域稀疏信號的準確性、減小重構(gòu)復(fù)雜度、擴展前端硬件的局限性。 3.提出一種基于塊壓縮感知的OFDM信號檢測方法,利用OFDM信號在頻域的結(jié)構(gòu)化特征,挖掘信號的塊稀疏特性,用更具結(jié)構(gòu)性的測量矩陣取代隨機測量方式,對較低塊稀疏度的OFDM信號進行檢測,以解決用壓縮感知理論對此類信號采樣恢復(fù)的高復(fù)雜度和低準確性的問題。 該方法通過對已知的具有特殊結(jié)構(gòu)的信號的研究,根據(jù)源信號的先驗信息,分析OFDM信號的結(jié)構(gòu)化特征,構(gòu)建源信號的塊稀疏模型,設(shè)計與信號結(jié)構(gòu)相匹配的測量矩陣,通過對結(jié)構(gòu)化信號進行聯(lián)合子空間向量的變換,以獲得盡量少的測量數(shù)據(jù)。通過仿真,與傳統(tǒng)方法相對比,所提出的檢測方法提高了塊稀疏信號檢測準確性、減小OFDM信號的重構(gòu)復(fù)雜度,將標準的稀疏度的前提條件擴展到包含更加豐富的信號類型。
[Abstract]:The cognitive radio system is one of the most important problems in cognitive radio system .

the purpose of spectrum detection is twofold : one is to sense the user to avoid interference to authorized users by switching to other available frequency bands or by limiting their interference to authorized users to an acceptable range ;
Second , the perceptual user should effectively utilize the frequency spectrum cavity to meet the system performance requirements . Therefore , the spectral perception accuracy in the cognitive radio has a crucial influence on the performance of the primary user and the secondary user .

This paper studies the spectrum detection technology from the aspects of frequency spectrum energy , frequency spectrum characteristic , cooperative detection and so on , and puts forward some algorithms such as energy detection , matching filter detection , circular feature detection and cooperative spectrum detection .

The compressed sensing theory brings revolutionary breakthrough to the data acquisition technology , combines the compression sensing theory with the broadband spectrum detection technology , and can solve the problem of insufficient sampling frequency of the ADC hardware . Based on the " compression sensing " , the broadband spectrum detection technology is a novel signal processing technique .

Aiming at the characteristics of spectrum environment dynamics , heterogeneous and broadband in the broadband cognitive radio system , the paper takes the compression - sensing theory as the entry point , analyzes the inherent relationship between the analog - to - analog information converter and the wide - band heterogeneous spectrum , analyzes the inherent coupling mechanism of the source signal sparse model and the signal - prior information , and introduces a broad - band spectrum detection strategy applicable to the current cognitive radio architecture and conforms to the characteristic of the spectrum of the specific signal .

The main innovations and contributions of this article are as follows :

The invention provides a non - sparse signal pilot detection method based on compression perception , which is based on a spectral estimation algorithm in a compressed domain , directly uses the observation value obtained by the compression perception to carry out frequency spectrum detection , thereby reducing the data amount and the algorithm complexity and reducing the detection time delay so as to solve the contradiction between the limited computing capacity of the cognitive node and the computational complexity of the compression perception recovery algorithm .

In the framework of the compression perception theory , the frequency spectrum of the main user is divided into a priori information , and a pilot pattern matched with the division structure is designed .

The invention provides a source signal frequency domain sampling structure under a compression sensing framework , which is a signal sampling method based on a frequency domain sampling theory .

A frequency - domain random demodulator ( FRD ) with transform matrix is designed by the research of the existing sampling structure . The structure is pre - processed by means of multi - channel parallel channel , and the original signal is pre - processed by randomizing different order frequency domain sampling signal , so that the sampling of the signal can be carried out in the frequency domain , and the result is compared with the existing sampler . The proposed FRD sampling structure can sample the signal in frequency domain , improve the accuracy of the sparse signal in frequency domain , reduce the reconstruction complexity and expand the limitation of the front end hardware .

3 . An OFDM signal detection method based on block compression perception is presented , which uses the structured feature of the OFDM signal in the frequency domain , the block sparse characteristic of the mining signal , the random measurement mode is replaced with a more structured measurement matrix , and the OFDM signal with lower block sparsity is detected to solve the problem of high complexity and low accuracy of the sampling recovery of the signal by using the compression perception theory .

the method improves the detection accuracy of the block sparse signal , reduces the reconstruction complexity of the OFDM signal , and extends the precondition of the standard sparsity to a signal type containing more abundant signal .
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN92

【參考文獻】

相關(guān)期刊論文 前1條

1 孫洪;張智林;余磊;;從稀疏到結(jié)構(gòu)化稀疏:貝葉斯方法[J];信號處理;2012年06期

，

本文編號：2016632