本文選題：寬帶頻譜感知　切入點：壓縮感知　出處：《國防科學技術(shù)大學》2014年博士論文

【摘要】：頻譜感知(Spectrum Sensing)技術(shù)旨在尋找授權(quán)頻帶內(nèi)未被使用的空閑頻譜資源,是利用動態(tài)頻譜接入機制解決可分配頻譜資源日益匱乏與授權(quán)頻帶被大量閑置之間矛盾的前提條件。隨著無線電技術(shù)與業(yè)務的迅猛發(fā)展,寬帶頻譜感知技術(shù)因其能提供更靈活的接入選擇等諸多優(yōu)點受到業(yè)界的廣泛關(guān)注,已成為頻譜感知研究中一個非常重要的發(fā)展方向。然而經(jīng)典的奈奎斯特采樣定理要求以兩倍于信號最高頻率的采樣率獲取采樣數(shù)據(jù),因此寬帶頻譜感知需要極高的采樣速率,從而對模數(shù)轉(zhuǎn)換器的實現(xiàn)以及采樣數(shù)據(jù)的存儲提出了嚴峻挑戰(zhàn)。通過發(fā)掘頻譜資源利用率低而具備的稀疏性,寬帶頻譜壓縮感知技術(shù)將壓縮感知理論與寬帶頻譜感知研究相結(jié)合,有效地降低了寬帶頻譜感知對采樣速率的要求,從而解決了過高采樣速率帶來的一系列問題。目前,寬帶頻譜壓縮感知的研究雖然取得了一定的進展,但仍然存在著許多問題有待解決。論文主要圍繞著寬帶頻譜壓縮感知研究中存在的若干問題進行討論、展開研究,旨在對不同的感知場景設計有效、可靠的寬帶頻譜壓縮感知算法。本文的主要研究成果如下:(1)在單節(jié)點寬帶頻譜壓縮感知算法研究方面,針對無線衰落信道以及噪聲不確定性等諸多不利因素帶來的感知性能不理想等問題,通過發(fā)掘?qū)嶋H應用場景中除了寬帶頻譜稀疏性之外的一些較易獲得的先驗信息,如部分信道占用狀態(tài)、信道占用概率以及信道劃分信息,分別提出了基于部分占用狀態(tài)的寬帶頻譜壓縮感知算法、基于占用概率的增強型寬帶頻譜壓縮感知算法以及基于非零子塊迭代檢測的寬帶頻譜壓縮感知算法,并仿真了所提算法在無噪聲和有噪聲時的性能。仿真結(jié)果表明所提算法均能有效地利用相應的先驗信息,顯著地提高單節(jié)點寬帶頻譜壓縮感知算法的頻譜重構(gòu)和頻譜感知性能。此外,仿真實驗還討論了非理想先驗信息以及算法參數(shù)對所提算法的影響。(2)在多節(jié)點合作式寬帶頻譜壓縮感知算法研究方面,針對現(xiàn)有算法大多存在的合作開銷過大問題,利用多節(jié)點合作式感知場景中單個感知節(jié)點接收信號的大動態(tài)范圍特征及不同節(jié)點接收信號的聯(lián)合稀疏特性,提出了基于支集融合的分布式寬帶頻譜壓縮感知算法,算法采用迭代機制,利用鄰近節(jié)點之間的局部通信自適應地獲得可靠的融合支集信息,并將其作為先驗信息參與下一次迭代時的本地頻譜重構(gòu)。仿真實驗以現(xiàn)有的分布式算法作為參照,比較了所提算法的精確重構(gòu)概率、檢測概率、計算復雜度和通信負擔。仿真結(jié)果表明所提算法能夠以較低的合作開銷,獲得較好的頻譜重構(gòu)和頻譜感知性能。此外,仿真實驗還分析了所提算法對算法參數(shù)選取的穩(wěn)定性。(3)在多節(jié)點合作式寬帶頻譜壓縮感知算法研究方面,分析了現(xiàn)有算法由于均涉及頻譜重構(gòu)過程,因而普遍計算量過大且需要一些諸如環(huán)境噪聲能量或者接收信號稀疏度等難以獲得的額外信息,嚴重限制了其應用范圍。鑒于寬帶頻譜感知通常僅關(guān)注于獲得整個寬帶頻譜的占用狀態(tài),提出了基于Karcher均值的分布式寬帶頻譜壓縮感知算法,算法中使用聯(lián)合稀疏信號的Karcher均值作為表征頻譜占用狀態(tài)的統(tǒng)計量,實現(xiàn)了從壓縮采樣數(shù)據(jù)到頻譜占用狀態(tài)的直接估計,從而省去了需耗費大量運算資源的頻譜重構(gòu)過程。為節(jié)省單個節(jié)點有限的通信資源,設計了一種分布式交替乘子法,僅利用鄰近節(jié)點之間的局部通信,以分布式計算的方式實現(xiàn)了整個感知過程。仿真實驗以現(xiàn)有的分布式算法作為參照,比較了所提算法的檢測概率、計算復雜度和通信負擔,仿真結(jié)果表明無需額外信息的所提算法能夠以較低的通信資源開銷和極低的計算資源開銷獲得較好的頻譜感知性能。此外,仿真實驗還測試了算法參數(shù)和感知網(wǎng)絡參數(shù)對所提算法性能的影響。(4)針對復雜電磁環(huán)境中寬帶非平穩(wěn)信號增多導致的頻域不再具備稀疏性的情況,分析了現(xiàn)有寬帶頻譜壓縮感知算法將由于頻域稀疏性不復存在而無法應用。通過將感知域從頻域擴展到時頻域,發(fā)掘感知對象在時頻域上的稀疏性,提出了一種基于短時傅里葉變換的時頻域信息壓縮感知算法,從以遠低于奈奎斯特采樣率獲取的壓縮采樣數(shù)據(jù)重構(gòu)出短時傅里葉變換時頻域信息,并以典型的寬帶非平穩(wěn)信號作為感知對象,仿真了所提算法的時頻域信息重構(gòu)性能。仿真結(jié)果表明,所提算法能夠以較低的采樣開銷獲得性能較好的短時傅里葉變換時頻域信息。
[Abstract]:Spectrum sensing technology (Spectrum Sensing) in order to find the authorization of the idle spectrum resources within the band is not in use, is the use of dynamic spectrum access mechanism to solve the distribution of spectrum resources shortage and contradiction is the premise condition of authorized frequency band between a large number of idle. With the rapid development of radio technology and business, the wideband spectrum sensing technology because it can provide access selection more flexible advantages such as attention by the industry, has become a very important research direction of spectrum sensing. However the classical Nyquist sampling theorem to two times the highest frequency of the signal sampling rate to obtain sample data, so the wideband spectrum sensing requires a high sampling rate of ADC and the realization of the sampling data storage challenges. By exploiting the low utilization of spectrum sparsity and ability, wide band The spectrum of compressed sensing technology will be compressed sensing theory and Research on wideband spectrum sensing combination, effectively reducing the sampling rate for wideband spectrum sensing requirements, in order to solve a series of problems brought by the high sampling rate. At present, the research of broadband spectrum compressed sensing has made some progress, but there are still many problems to be solved. This thesis mainly focuses on the broadband spectrum compression problems in the study of perception are discussed, studied, aimed at the effective design of the perception of different scenes, the broadband spectrum reliable compressed sensing algorithm. The main research results are as follows: (1) in the compressed sensing algorithm of single node spectrum sensing performance, the problem for wireless fading channel and noise uncertainty and many other adverse factors is not ideal, by exploring the practical application in the broadband frequency Some of the spectrum is easy to obtain the prior information of sparse outside, as part of the channel occupation, occupation probability and channel division of information channel are proposed for wideband spectrum partial occupancy state of compressed sensing algorithm based on the probability of occupation enhanced broadband spectrum compressed sensing algorithm and broadband spectrum non zero block iterative detection of compressed sensing algorithm based on the simulation, and the performance of the algorithm in the absence of noise and noise. The simulation results show that the proposed algorithm can effectively use the prior information, spectrum reconstruction and spectrum sensing performance of compressed sensing algorithm of single node broadband frequency significantly improved. In addition, the simulation experiment the effect of non ideal prior information and the algorithm parameters of the proposed method is also discussed. (2) in the compressed sensing algorithm of multi node cooperative wideband spectrum, most of the existing algorithms The cooperation overhead problem, using joint sparse characteristics of the large dynamic range of the received signal characteristics of single node and multi node cooperative sensing scenarios and different nodes of the received signal, we propose a distributed broadband spectrum support fusion compressed sensing algorithm based on iterative algorithm using local adaptive mechanism, communication between neighboring nodes to obtain reliable the support of information fusion, and as a priori information in local spectrum reconstruction of the next iteration. Simulation experiments with existing distributed algorithms as a reference, accurate reconstruction probability, the proposed algorithm of detection probability, computational complexity and communication burden. The simulation results show that the proposed algorithm can lower the cooperation overhead, to obtain the spectral reconstruction and spectrum sensing performance. In addition, the simulation experiment is also analyzed the stability of the algorithm of selecting the parameters of the algorithm (3). In the compressed sensing algorithm of multi node cooperative wideband spectrum, analysis of the existing algorithms are involved in the reconstruction process because of the spectrum, so it is generally too large amount of calculation and some environmental noise such as energy or receiving the signal sparsity is difficult to obtain additional information, severely limits the scope of its application. In view of the wideband spectrum sensing usually only focus on the the broadband spectrum occupancy, we propose a distributed broadband spectrum mean Karcher compressed sensing algorithm based on the combination of sparse signal algorithm Karcher mean as characterization of spectrum occupancy statistics, from the implementation of the direct compression sampling data to estimate spectrum occupancy, spectrum reconstruction process which eliminates the need to spend a lot of computing resources in order to save communication resources. A single node is limited, the design of a distributed alternating multiplier method, using only the adjacent Local communication between the nodes in distributed computing mode to realize the whole process of perception. The simulation experiment with the existing distributed algorithms as a reference, the proposed detection probability algorithm, computational complexity and communication burden, the simulation results show that without additional information of the proposed algorithm is able to communication and very low resource cost low computational resource overhead spectrum obtained good performance. In addition, the simulation experiment also tested the effect of algorithm parameters and sensing network parameters on the performance of the proposed algorithm. (4) based on frequency domain broadband non-stationary signal in complicated electromagnetic environment to increase the no longer have the sparsity, the analysis of the existing wideband spectrum compressive sensing algorithm due to the frequency domain sparsity does not exist and cannot be used. The sensing domain from frequency domain extension in time-frequency domain, sparse frequency domain to explore perceived objects when presented A time domain and frequency domain information of short-time Fourier transform algorithm based on compressed sensing, lower than the Nyquist sampling rate gets compressed frequency sampling data to reconstruct the Fourier transform from beyond, with a typical broadband nonstationary signal as the sense objects, the proposed algorithm of time domain information reconstruction performance simulation. The simulation results show that the the proposed algorithm can lower the sampling cost performance of short time Fourier transform good time domain information.

【學位授予單位】：國防科學技術(shù)大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TN925

【參考文獻】

相關(guān)期刊論文 前1條

1 石光明;劉丹華;高大化;劉哲;林杰;王良君;;壓縮感知理論及其研究進展[J];電子學報;2009年05期

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本文編號：1714842