【摘要】：隨著人們對移動通信的需求不斷增長，頻譜資源的分配就變得越來越困難，而認(rèn)知無線電技術(shù)可以解決這一難題。頻譜感知作為認(rèn)知無線電技術(shù)的關(guān)鍵，其目的是檢測頻譜空穴。傳統(tǒng)的頻譜感知只能對單個頻段進行感知，為了提高檢測效率，出現(xiàn)了寬帶頻譜感知技術(shù)。在對寬帶信號進行感知時，極高的采樣速率成為了限制這一技術(shù)的瓶頸，利用壓縮感知方法可以解決這一難題。貝葉斯方法是近些年提出的一類壓縮感知算法，它可以利用不同的先驗概率靈活地構(gòu)建稀疏信號的恢復(fù)模型，還能給出恢復(fù)信號的誤差范圍，具有優(yōu)越的性能。因此本文的重點就是適用于寬帶頻譜感知的貝葉斯壓縮感知恢復(fù)算法研究。 本文首先介紹了貝葉斯建模的過程，然后對使用拉普拉斯先驗的壓縮感知算法進行了改進，提出了一種快速算法——L-BSC算法，并給出詳細流程。同時，本文將一種在貝葉斯框架下的自適應(yīng)觀測矩陣設(shè)計方法與提出的快速算法結(jié)合在一起，得到一種自適應(yīng)的快速算法。仿真結(jié)果表明，這種自適應(yīng)的快速貝葉斯算法不但在恢復(fù)一般信號時性能良好，應(yīng)用于寬帶頻譜感知場景時也能獲得很高的頻譜重構(gòu)精度，具有優(yōu)良的頻譜檢測性能。因此認(rèn)為該算法性能良好，適合應(yīng)用于寬帶壓縮頻譜感知中。 考慮到寬帶頻譜感知中頻譜分配造成的頻域塊稀疏結(jié)構(gòu)，本文引入了塊稀疏貝葉斯學(xué)習(xí)（Block Sparse Bayesian Learning, BSBL）框架，并介紹了兩種基于該框架提出的算法。在此基礎(chǔ)上，本文提出了一種將BSBL算法和group lasso方法相結(jié)合的算法——BSBL-Group Lasso算法。該算法大大減少了迭代次數(shù)，提高了執(zhí)行效率，并且保證良好的算法性能。為了解決某些時候不能獲得信號塊分布情況的問題，本文擴展了BSBL框架，得到一種應(yīng)用于信號塊分布未知情況下的模型，并在此基礎(chǔ)上對算法進行了改進，得到了BSBL-EEM和BSBL-EBO算法。仿真結(jié)果顯示，BSBL-Group Lasso算法在恢復(fù)塊稀疏信號時可獲得較高的恢復(fù)精度，，在寬帶壓縮頻譜感知中可獲得優(yōu)良的檢測性能。而BSBL-EEM和BSBL-EBO算法在信號的塊分布未知的情況下，即使用戶定義的分塊情況與實際信號不一致也可獲得很高的恢復(fù)精度，應(yīng)用范圍很廣。
[Abstract]:With the increasing demand for mobile communication, the allocation of spectrum resources becomes more and more difficult, and cognitive radio technology can solve this problem. Spectrum sensing, as the key of cognitive radio technology, aims to detect spectral holes. Traditional spectrum sensing can only perceive a single frequency band. In order to improve detection efficiency, broadband spectrum sensing technology has emerged. The extremely high sampling rate becomes the bottleneck of this technique when we perceive the wideband signal. The compression sensing method can solve this problem. Bayesian method is a kind of compression sensing algorithm proposed in recent years. It can flexibly construct the sparse signal recovery model with different prior probabilities, and it can also give the error range of the recovery signal, so it has superior performance. Therefore, the emphasis of this paper is the research of Bayesian compressed sensing restoration algorithm suitable for wideband spectrum sensing. This paper first introduces the process of Bayesian modeling, then improves the compression perception algorithm using Laplace priori, proposes a fast algorithm, L-BSC algorithm, and gives a detailed flow chart. At the same time, an adaptive observation matrix design method based on Bayesian framework is combined with the proposed fast algorithm to obtain an adaptive fast algorithm. Simulation results show that the adaptive fast Bayesian algorithm not only performs well in recovering general signals, but also achieves high spectral reconstruction accuracy when it is applied to wideband spectrum sensing scene, and has excellent spectrum detection performance. Therefore, the proposed algorithm has good performance and is suitable for wideband spectrum sensing. Considering the frequency domain block sparse structure caused by spectrum allocation in wideband spectrum sensing, a block sparse Bayesian learning (Block Sparse Bayesian Learning, BSBL) framework is introduced, and two algorithms based on this framework are introduced. On this basis, this paper proposes a new algorithm, BSBL-Group Lasso algorithm, which combines BSBL algorithm with group lasso algorithm. The algorithm greatly reduces the number of iterations, improves the execution efficiency and ensures good performance. In order to solve the problem that the signal block distribution can not be obtained at some time, this paper extends the BSBL framework, and obtains a model which is applied to the unknown signal block distribution, and then improves the algorithm. BSBL-EEM and BSBL-EBO algorithms are obtained. Simulation results show that the BSBL-Group Lasso algorithm can achieve high recovery accuracy when restoring block sparse signals, and it can obtain excellent detection performance in wideband compressed spectrum sensing. The BSBL-EEM and BSBL-EBO algorithms can obtain high recovery accuracy even if the user-defined block is not consistent with the actual signal even if the block distribution of the signal is unknown and has a wide range of applications.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.7

【參考文獻】

相關(guān)期刊論文 前6條

1 江曉林;顧學(xué)邁;何晨;;基于壓縮感知的聯(lián)合協(xié)作頻譜感知算法[J];上海交通大學(xué)學(xué)報;2013年07期

2 甘偉;許錄平;張華;蘇哲;;一種貪婪自適應(yīng)壓縮感知重構(gòu)[J];西安電子科技大學(xué)學(xué)報;2012年03期

3 付寧;曹離然;彭喜元;;基于子空間的塊稀疏信號壓縮感知重構(gòu)算法[J];電子學(xué)報;2011年10期

4 楊俊杰;劉海林;;增廣Lagrange函數(shù)優(yōu)化算法在稀疏信號重構(gòu)問題中的應(yīng)用[J];計算機科學(xué);2011年09期

5 王國富;張海如;張法全;徐婷;;基于改進遺傳算法的正交匹配追蹤信號重建方法[J];系統(tǒng)工程與電子技術(shù);2011年05期

6 李樹濤;魏丹;;壓縮傳感綜述[J];自動化學(xué)報;2009年11期

相關(guān)碩士學(xué)位論文 前1條

1 劉亞新;用于壓縮感知信號重建的算法研究[D];北京交通大學(xué);2010年

本文編號：2333057