本文關(guān)鍵詞：基于稀疏分解的頻譜感知方法研究　出處：《哈爾濱工業(yè)大學(xué)》2015年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 稀疏分解 頻譜感知 分布式壓縮感知 過(guò)完備字典 字典訓(xùn)練

【摘要】：近年來(lái),隨著各種無(wú)線電新技術(shù)和新業(yè)務(wù)的廣泛應(yīng)用以及通信技術(shù)的迅速發(fā)展,對(duì)頻譜資源的需求程度和數(shù)量日益增長(zhǎng),如何在頻譜資源有限的情況下對(duì)其更為有效的利用成為一個(gè)急需解決的問(wèn)題。認(rèn)知無(wú)線電技術(shù)的提出為我們解決這個(gè)問(wèn)題提供了方法。頻譜感知技術(shù)是認(rèn)知無(wú)線電的基礎(chǔ)環(huán)節(jié),它的好壞關(guān)系著系統(tǒng)性能的優(yōu)劣。稀疏分解可以抓住信號(hào)內(nèi)部的主要特征,提取信號(hào)成分,具有去噪的功能;近年來(lái)興起的壓縮感知技術(shù)也是以信號(hào)能夠在變換域上稀疏分解為前提的,它能夠突破奈奎斯特采樣定理的限制,僅通過(guò)少量的壓縮測(cè)量值便能夠?qū)崿F(xiàn)信號(hào)的重構(gòu)。本文對(duì)基于稀疏分解的頻譜感知方法進(jìn)行了研究。在頻譜能量檢測(cè)方式中,門(mén)限的設(shè)置與接收信號(hào)的信噪比直接相關(guān),因此,接收信號(hào)的信噪比是影響頻譜感知性能的一個(gè)決定性因素。在一定范圍內(nèi),隨著信噪比的降低,恒虛警條件下的檢測(cè)概率快速下降。在本文中,考慮到稀疏分解有一定程度的去噪作用,因此將稀疏分解引入到接收機(jī)前端,在接收信號(hào)后,首先進(jìn)行去噪處理,再進(jìn)入后端的頻譜感知部分。在經(jīng)過(guò)稀疏分解去噪的過(guò)程后,隨著信噪比的改善,在目前的信噪比下設(shè)定新的門(mén)限,檢測(cè)性能必然有很大程度的提高。在認(rèn)知MIMO系統(tǒng)中,多天線提供了更高的檢測(cè)可靠性,但也帶來(lái)了采樣數(shù)據(jù)量的急劇提升,而分布式壓縮感知技術(shù)正是基于多信號(hào)的聯(lián)合采樣技術(shù),降低采樣的數(shù)據(jù)量。它要求在多信號(hào)的基礎(chǔ)上滿(mǎn)足聯(lián)合稀疏模型,而MIMO技術(shù)由于天線之間的相關(guān)性,剛好滿(mǎn)足JSM-2模型,因此基于認(rèn)知MIMO的分布式壓縮感知技術(shù)迫切需要一個(gè)針對(duì)多天線環(huán)境的聯(lián)合稀疏字典。字典訓(xùn)練算法作為一種較為新穎的字典獲取方法,只給出了單信源訓(xùn)練信號(hào)的情況下如何獲得訓(xùn)練字典。本文結(jié)合字典訓(xùn)練算法與多天線下的聯(lián)合稀疏模型,將普通的字典訓(xùn)練算法拓展為三種不同合并方式下的多天線聯(lián)合訓(xùn)練字典算法。相比于一般的字典訓(xùn)練算法,聯(lián)合字典訓(xùn)練算法能夠在同樣的訓(xùn)練次數(shù)的情況下,獲得更好的稀疏表示效果。因此,在分布式壓縮感知重構(gòu)之后,重構(gòu)概率顯著提高,之后的頻譜檢測(cè)性能也有了進(jìn)一步的提升�；诼�(lián)合訓(xùn)練字典的頻譜感知技術(shù)在提高系統(tǒng)檢測(cè)可靠性的基礎(chǔ)上,也降低了采樣數(shù)據(jù)量。
[Abstract]:In recent years, with the wide application of various new radio technologies and new services and the rapid development of communication technology, the demand for spectrum resources is increasing day by day. How to make more effective use of spectrum resources under the condition of limited spectrum resources is an urgent problem to be solved. The development of cognitive radio technology provides a method for us to solve this problem. Spectrum sensing technology is cognitive wireless. The foundation of electricity. The sparse decomposition can grasp the main characteristics of the signal, extract the signal components, and have the function of denoising. The compression sensing technology developed in recent years is also based on the sparse decomposition of signals in the transform domain, which can break through the limitation of Nyquist sampling theorem. Only a small amount of compressed measurements can be used to reconstruct the signal. In this paper, the spectral sensing method based on sparse decomposition is studied. The threshold setting is directly related to the signal-to-noise ratio of the received signal. Therefore, the SNR of the received signal is a decisive factor affecting the spectrum sensing performance. In a certain range, the SNR decreases with the decrease of SNR. In this paper, considering the sparse decomposition has a certain degree of de-noising, the sparse decomposition is introduced to the front end of the receiver, after receiving the signal. After the sparse decomposition and denoising process, with the improvement of SNR, a new threshold is set under the current SNR. In cognitive MIMO systems, multiple antennas provide higher detection reliability, but also bring a sharp increase in the sample data. Distributed compression sensing technology is based on multi-signal joint sampling technology to reduce the amount of data sampled, it requires that the multi-signal on the basis of the United sparse model. Because of the correlation between antennas, MIMO technology just meets the JSM-2 model. Therefore, distributed compression sensing technology based on cognitive MIMO urgently needs a joint sparse dictionary for multi-antenna environment. Dictionary training algorithm is a novel dictionary acquisition method. Only how to obtain the training dictionary under the condition of single source training signal is given. This paper combines the dictionary training algorithm with the joint sparse model under multiple antennas. The common dictionary training algorithm is extended to the multi-antenna joint training dictionary algorithm under three different combinations, compared with the general dictionary training algorithm. The joint dictionary training algorithm can obtain better sparse representation under the same training times. Therefore, after distributed compression awareness reconstruction, the reconstruction probability is improved significantly. The spectrum sensing technology based on the joint training dictionary can improve the reliability of the system and reduce the sample data.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TN925

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本文編號(hào)：1396262