基于頻域稀疏特性的頻譜感知算法研究
發(fā)布時間：2019-04-17 09:03
【摘要】：隨著無線通信技術(shù)的迅速發(fā)展,靜態(tài)的頻譜分配策略和使用方式已不能滿足其高速率、高質(zhì)量的通信需求。而認知無線電系統(tǒng)為動態(tài)分配和使用頻譜提供新的方案,自1999年提出以來,受到了廣泛關(guān)注和研究。作為認知無線電的核心技術(shù),頻譜檢測算法通過實時準確的識別“頻譜空穴”,為非授權(quán)用戶提供接入機會,進而提高頻譜利用效率。信號在某些域具有的稀疏特性,一方面可以更加集中信號的能量,另一方面也可以有效降低采樣速率和難度,分別對應(yīng)頻譜檢測算法中對高檢測概率和動態(tài)實時檢測的要求。所以,本文嘗試將稀疏特性和檢測算法結(jié)合,著重研究基于頻域稀疏特性的檢測算法,并對算法的檢測性能進行分析。首先,論文介紹了課題的研究背景和意義,分析現(xiàn)有檢測算法的優(yōu)勢和缺陷,引出本文重點研究的基于功率譜估計最大值的檢測算法和基于MWC-SBL系統(tǒng)的檢測算法,接著總結(jié)了這兩種算法所用到的基礎(chǔ)理論,包括功率譜估計、隨機帶寬轉(zhuǎn)換器、稀疏貝葉斯學(xué)習等,并給出了相應(yīng)的仿真結(jié)果。其次,深入分析基于功率譜估計最大值的檢測算法,定義統(tǒng)計判決變量為信號功率譜估計最大值。在分析統(tǒng)計判決變量的均值、方差、相關(guān)性等統(tǒng)計特性之后,利用卡方分布對H0、H1假設(shè)下的統(tǒng)計判決變量進行分布建模,推導(dǎo)檢測概率、虛警概率、判決門限等理論表達式。仿真部分,驗證卡方分布建模的正確性和選取估計最大值作為統(tǒng)計判決變量的優(yōu)勢,分析采樣數(shù)據(jù)長度、功率譜分段數(shù)目對檢測性能的影響,并與能量檢測算法進行對比。隨后的仿真表明,不同的窗函數(shù)選取對檢測概率和卡方分布建模的準確性都有影響且二者呈現(xiàn)相反的趨勢。盡管基于功率譜估計最大值的算法在稀疏窄帶信號的檢測中有著獨特的優(yōu)勢,但由于前端采樣的限制,對于稀疏多頻帶信號并不能達到及時檢測的要求。最后,研究了基于MWC-SBL系統(tǒng)的檢測算法,以壓縮采樣為基礎(chǔ),利用稀疏貝葉斯學(xué)習算法,充分挖掘觀測數(shù)據(jù)的信息,為寬帶檢測提供另一種方案。定義支撐集為統(tǒng)計判決變量,通過支撐集的檢測情況,完成每個頻帶信號存在與否的判斷。在完成算法描述后,根據(jù)恢復(fù)得到的支撐集與原始支撐集之間的關(guān)系,定義檢測概率、虛警概率的統(tǒng)計表達式。隨后的仿真重點關(guān)注固定位置單信號和多信號的檢測性能、隨機位置單信號和多信號的檢測性能、不同匹配原則對檢測性能的提升以及均方誤差等指標,并與基于MWC-OMP的檢測算法進行對比。仿真表明,基于MWC-SBL檢測算法在檢測概率等性能指標上比MWC-OMP檢測算法更具優(yōu)勢。
[Abstract]:With the rapid development of wireless communication technology, static spectrum allocation strategy and usage mode can no longer meet its high-speed, high-quality communication needs. Cognitive radio system provides a new scheme for dynamic allocation and use of spectrum. Since it was put forward in 1999, it has received extensive attention and research. As the core technology of cognitive radio, spectrum detection algorithm can identify "spectrum holes" in real-time and accurately, so as to provide access opportunities for unauthorized users and improve the efficiency of spectrum utilization. The sparse characteristics of the signal in some domains can concentrate the energy of the signal on the one hand, on the other hand, it can also effectively reduce the sampling rate and difficulty, corresponding to the requirements of high detection probability and dynamic real-time detection in the spectrum detection algorithm respectively. Therefore, this paper attempts to combine sparse characteristics with detection algorithms, focusing on the detection algorithm based on frequency domain sparse characteristics, and analyzes the detection performance of the algorithm. Firstly, the paper introduces the research background and significance of the subject, analyzes the advantages and disadvantages of the existing detection algorithms, and leads to the detection algorithm based on the maximum power spectrum estimation and the detection algorithm based on MWC-SBL system. Then the basic theories used in these two algorithms are summarized, including power spectrum estimation, random bandwidth converter, sparse Bayesian learning, and the corresponding simulation results are given. Secondly, the detection algorithm based on the maximum value of power spectrum estimation is deeply analyzed, and the statistical decision variable is defined as the maximum value of signal power spectrum estimation. After analyzing the statistical characteristics of statistical decision variables, such as mean value, variance and correlation, the statistical decision variables under H _ 0 and H _ 1 assumptions are modeled by chi-square distribution, and the theoretical expressions of detection probability, false alarm probability and decision threshold are derived. In the simulation part, the correctness of the chi-square distribution modeling and the advantage of selecting the estimated maximum value as the statistical decision variable are verified. The effects of the length of sampling data and the number of power spectrum segments on the detection performance are analyzed, and compared with the energy detection algorithm. The simulation results show that different window functions have an effect on the accuracy of detection probability and chi-square distribution modeling, and they show opposite trend. Although the algorithm based on power spectrum estimation has unique advantages in the detection of sparse narrow-band signals, due to the limitation of front-end sampling, sparse multi-band signals can not meet the requirements of timely detection. Finally, the detection algorithm based on MWC-SBL system is studied. On the basis of compressed sampling, sparse Bayesian learning algorithm is used to fully mine the information of observed data, which provides another scheme for broadband detection. The support set is defined as a statistical decision variable. Through the detection of the support set, the existence of each frequency band signal is judged. After the algorithm is described, the statistical expressions of detection probability and false alarm probability are defined according to the relationship between the restored support set and the original support set. The following simulation focuses on the detection performance of fixed position single signal and multi-signal, the detection performance of random position single signal and multi-signal, the improvement of detection performance by different matching principles and the mean square error (MSE), and so on. And compared with the detection algorithm based on MWC-OMP. Simulation results show that the MWC-SBL-based detection algorithm has more advantages than the MWC-OMP detection algorithm in terms of detection probability and other performance indicators.
【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】：2017
【分類號】：TN925
本文編號：2459290
[Abstract]:With the rapid development of wireless communication technology, static spectrum allocation strategy and usage mode can no longer meet its high-speed, high-quality communication needs. Cognitive radio system provides a new scheme for dynamic allocation and use of spectrum. Since it was put forward in 1999, it has received extensive attention and research. As the core technology of cognitive radio, spectrum detection algorithm can identify "spectrum holes" in real-time and accurately, so as to provide access opportunities for unauthorized users and improve the efficiency of spectrum utilization. The sparse characteristics of the signal in some domains can concentrate the energy of the signal on the one hand, on the other hand, it can also effectively reduce the sampling rate and difficulty, corresponding to the requirements of high detection probability and dynamic real-time detection in the spectrum detection algorithm respectively. Therefore, this paper attempts to combine sparse characteristics with detection algorithms, focusing on the detection algorithm based on frequency domain sparse characteristics, and analyzes the detection performance of the algorithm. Firstly, the paper introduces the research background and significance of the subject, analyzes the advantages and disadvantages of the existing detection algorithms, and leads to the detection algorithm based on the maximum power spectrum estimation and the detection algorithm based on MWC-SBL system. Then the basic theories used in these two algorithms are summarized, including power spectrum estimation, random bandwidth converter, sparse Bayesian learning, and the corresponding simulation results are given. Secondly, the detection algorithm based on the maximum value of power spectrum estimation is deeply analyzed, and the statistical decision variable is defined as the maximum value of signal power spectrum estimation. After analyzing the statistical characteristics of statistical decision variables, such as mean value, variance and correlation, the statistical decision variables under H _ 0 and H _ 1 assumptions are modeled by chi-square distribution, and the theoretical expressions of detection probability, false alarm probability and decision threshold are derived. In the simulation part, the correctness of the chi-square distribution modeling and the advantage of selecting the estimated maximum value as the statistical decision variable are verified. The effects of the length of sampling data and the number of power spectrum segments on the detection performance are analyzed, and compared with the energy detection algorithm. The simulation results show that different window functions have an effect on the accuracy of detection probability and chi-square distribution modeling, and they show opposite trend. Although the algorithm based on power spectrum estimation has unique advantages in the detection of sparse narrow-band signals, due to the limitation of front-end sampling, sparse multi-band signals can not meet the requirements of timely detection. Finally, the detection algorithm based on MWC-SBL system is studied. On the basis of compressed sampling, sparse Bayesian learning algorithm is used to fully mine the information of observed data, which provides another scheme for broadband detection. The support set is defined as a statistical decision variable. Through the detection of the support set, the existence of each frequency band signal is judged. After the algorithm is described, the statistical expressions of detection probability and false alarm probability are defined according to the relationship between the restored support set and the original support set. The following simulation focuses on the detection performance of fixed position single signal and multi-signal, the detection performance of random position single signal and multi-signal, the improvement of detection performance by different matching principles and the mean square error (MSE), and so on. And compared with the detection algorithm based on MWC-OMP. Simulation results show that the MWC-SBL-based detection algorithm has more advantages than the MWC-OMP detection algorithm in terms of detection probability and other performance indicators.
【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】：2017
【分類號】：TN925
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