认知无线电中空闲频谱检测技术的研究
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摘要
随着无线通信技术的快速发展,目前宽带多媒体业务已经成为无线服务重点,而无线频谱资源的匮乏已成为实现高性能无线服务的严重屏障。动态频谱接入被认为是解决无线频谱资源紧张、利用率低下的有效途径。认知无线电技术能够动态利用时间和空间上空闲的无线授权频段,为新的无线应用提供可用的频谱资源,因此它被认为是未来实现动态频谱接入的理想载体。而空闲频谱检测作为认知无线电物理层的关键技术之一,它是认知无线电系统能够正常工作的前提。本文从快速、准确、高灵敏度的检测要求出发,对单感知用户频谱检测技术、多天线频谱检测技术、多感知用户合作频谱检测技术做了深入研究。
在第1章,首先对认知无线电的频谱检测的特殊性进行了讨论;然后对频谱检测技术进行了比较详细的分类;并对各种频谱检测方法的优缺点进行了归纳总结;此外,还对各种方法中具有代表性的文献的内容要点进行了阐述。
在第2章,首先介绍了信号检测的基本概念,基本准则。然后为了克服能量检测方法易受噪声功率不确定性影响的严重缺点,提出了一种基于广义似然比准则的频谱检测算法,该算法首先通过最大似然准则分别对两种假设下的似然函数中的未知参数进行估计,然后按照似然比检验准则产生一个与噪声功率无关的检测统计量。通过数学推导和仿真实验都证明了该检测统计量在任何信道条件下都具有良好的抗噪声不确定性的优点。当存在噪声不确定性时,仿真结果还表明在接收数据长度和信噪比相同的条件下,本文GLRT算法的检测概率将明显高于能量检测法。
在第3章,首先对循环谱的基本理论、估计方法进行了阐述。然后介绍了Dandawate和Giannakis提出的循环谱统计量检测算法。最后从降低循环谱检测统计量构造复杂度、如何设置判决门限、提高检测概率的角度出发,提出了一种基于频域平滑的循环谱统计量(FSCSS)频谱检测算法,在对检测统计量的概率分布进行了理论分析的基础之上,通过数学推导得到了高斯白噪声信道和瑞利衰落信道下,检测统计量虚警概率和检测概率的解析表达式,并解决了判决门限的设置问题。随后把在一个循环频率处构造检测统计量的方法推广到在两个循环频率处构造检测统计量的情景中。仿真实验的结果表明:在接收数据长度和信噪比相同的条件下,本文FSCSS算法的检测概率将高于DG算法。
在第4章,由于多天线具有良好的抗衰落特性,所以可以将它与其他物理层检测方法相结合,从而达到进一步提高检测性能的目的。首先将基于频域平滑的循环谱统计量检测算法与多天线技术相结合,推导了采用等增益合并后检测统计量虚警概率的解析表达式,解决了判决门限的设置问题。然后给出了基于能量检测的多天线等增益合并法分别在两种信道条件下,虚警概率和检测概率的解析表达式。接下来介绍了Quan等人提出的最优线性加权合并频谱检测算法,并将其应用到多天线场景中。最后通过仿真实验将本文FSCSS算法的检测性能与这两种算法进行了比较。仿真结果表明:在接收数据长度和信噪比相同的条件下,本文FSCSS算法的检测概率最高。
另外,在第4章中还从降低循环谱算法计算量的角度出发,对基于频域平滑的循环谱统计量频谱检测算法提出一种折衷方案;然后从使感知用户的平均吞吐量最大化的角度出发,研究了多天线下的折衷循环谱统计量频谱检测算法检测时长的优化问题。通过数学推导和仿真实验都证明了最佳检测时长的存在性。仿真实验还给出了不同天线个数和不同参数条件下吞吐量性能的对比曲线,仿真结果表明:通过增加天线个数不仅可以提高感知用户的最大平均吞吐量,而且还缩短了最佳检测时长。
在第5章,为了抵抗深度衰落、提高频谱检测的可靠性,提出了一种新的基于集中式判决融合的合作频谱检测算法。该算法从使多感知用户合作检测概率最大化的角度出发,利用序列二次规划法求解控制中心的优化融合准则和各感知用户优化判决门限。本文首先给出了合作检测算法的数学描述。然后求解了不同信道条件下合作检测算法中联合判决概率的表达式。接下来分别对合作检测算法的优化问题、求解方法、实施步骤进行了阐述。最后对2比特软判决合作检测算法和两个感知用户判决相关条件下合作检测算法的优化问题进行了讨论。仿真实验得到以下结果:第一,无论是从检测概率随信噪比变化的曲线,还是从接收机操作特性曲线都可以看出,与其他融合准则固定的合作检测算法相比,本文的优化合作检测算法的检测概率始终是最高的。第二,在检测概率相同的条件下,通过多用户合作,可以缩短检测时长。第三,在信噪比相同的条件下,2比特软判决相比于1比特硬判决优化合作检测算法而言,检测概率只有微小改善,而且随着感知用户的增加,改善量越来越小。第四,两个相关感知用户的相关系数越大,与它们相互独立时相比,合作检测概率的改善量损失越大。
With the rapid development of wireless communication technologies, wireless service is now focusing on broadband multimedia business. However, the lack of wireless spectrum is becoming the obstacle of implementing these service. Dynamic spectrum access is considered an effective approach to solve shortness of wireless spectrum resources and lowness of spectrum utilization. Cognitive radio can dynamically reuse those idle authorized spectrum in temporal and spatial domain, and provide usable spectrum resources for new wireless applications. Therefore, cognitive radio is considered as an ideal carrier of implementing dynamic spectrum access in the future. As an key technology of cognitive radio physical layer, idle spectrum detection is the premise of cognitive radio system to operate normally. Starting from the request of quickness, exactness, and sensitiveness by spectrum detection, This dissertation studys idle spectrum detection deeply.
In chapter one, first, the particularity of spectrum detection in cognitive radio is discussed. Second, the spectrum detection technologies are classified in detail. Third, the merit and shortcoming of various spectrum detection methods has been summrized,and the outline of representative literature of each spectrum method is expatiated at the same time.
In chapter two, first, it is introduced the based concepts and rules of signal detection. Second, to overcome the serious shortcoming of failing to noise uncertainty by energy detection, it is proposed a blind spectrum detection algorithm based on generalized likehood ratio rule by this dissertation. First, the unknown parameters of likehood function are estimated through maximum likehood ratio rule under two hypothesis. Then, it is produced a detection statistic which is not ralated to nosie power by likehood ratio test rule. This statistic is free of noise uncertainty through mathematical deduction and computer simulation. When the noise uncertainty exists, if the length of received data and SNR are the same, the simulation results show that the detection probability of GLRT algorithm exceeds the energy detection clearly.
In chapter three, first, the basic theory and esitimation methods of cyclic spectral are expatiated. Second, it is introduced the cyclic spectral statistic detection algorithm brought forward by Dandawate and Giannakis. Starting from deducing cyclic spectral detection statistic constructing complexity,how to set the threshold and improving detection probability, an cyclic spectral statistic spectrum detection algorithm based on freqency domain smoothing(FSCSS) is proposed by this dissertation. First, the distribution of detection statistic is analyzed theoretically. Second, it is deduced analytic expression of the false alarm probability and detection probability under AWGN channel and flat Rayleigh fading channel, the threshold setting is solved at the same time. Then, the FSCSS algorithm which has construct detection statistic at one cyclic frequency is extended to construct detection statistic at two frequency. The simulaiton results show when the length of received data and SNR are the same, the detection probability of FSCSS algorithm exceeds the DG algorithm.
In chapter four, because of the merit of resisting fading, the multi-antenna technology can be combined with other physical layer detection methods, so as to further improve detection probability. First, the cyclic spectral statistic spectrum detection algorithm based on freqency domain smoothing is combined with multi-antenna technology, then it is deduced analytic expression of false alarm probability, and the threshold setting is solved at the same time. Second, analytic expression of the false alarm probability and detection probability of multi-antenna equal gain combining energy detection method are present. Third, it is introduced the optimal linear weighted combining detection algorithm proposed by Quan etc. At last, the three detection algorithms are compared through simulaiton, the results show that, when the length of received data and SNR are the same, the detection probability of the proposed algorithm is the best of all.
In addition, starting from deducing calculation of cyclic spectral detection algorithm, a compromising scheme is proposed. Next, starting from maximizing the average throughput of sensing user, the optimization of detection time of compromising multi-antenna cyclic spectral statistic spectrum detection algorithm is study. It is proved the optimal detection time is existent through mathematical deduction and computer simulation. The simulation experiments give the performance curve of throughput under different number of antennas and paremeters. The simulation results show that increasing the number of antennas can not only improve the maximum average throughput, but also shorten the optimal detection time.
In chapter five, to resist deep fading and improve reliability of spectrum detection, a new cooperative spectrum detection algorithm based on centralized decision fusion is proposed. The proposed algorithm starts from maximizing the global cooperative detection, solves optimized the fusion rule of control center and thresholds of each cognitive user by sequential quadratic programming. First, the mathematical description of cooperative detection algorithm is present. Second, solution of uniting judging probability are given under different channel. Third, the optimizing problem of cooperative detection algorithm, solution method, implementation steps are expatiated. At last, the soft decision optimal cooperative spectrum detection algorithm and optimal cooperative spectrum detection algorithm under correlated decision are discussed. The simulation experiments get results as followed: first, whether from the curve of detection probability versus SNR, or from receiver operating characteristic curve, compared with other cooperative detection algorithm whose fusion is fixed, the detection probability of the proposed algorithm is always the best. Second, when the detection probability is the same, the detection time is shorter by users cooperation.Third, when SNR is the same, compared with hard decision algorithm, the detection probability of soft decision algorithm has only a little improvement. And with the raise of sensing users, the improvement is worse and worse. Fouth, compared with two users independent, the bigger the correlated coefficient of two users is, the bigger the loss of improvement of cooperative detection probability is.
在第1章,首先对认知无线电的频谱检测的特殊性进行了讨论;然后对频谱检测技术进行了比较详细的分类;并对各种频谱检测方法的优缺点进行了归纳总结;此外,还对各种方法中具有代表性的文献的内容要点进行了阐述。
在第2章,首先介绍了信号检测的基本概念,基本准则。然后为了克服能量检测方法易受噪声功率不确定性影响的严重缺点,提出了一种基于广义似然比准则的频谱检测算法,该算法首先通过最大似然准则分别对两种假设下的似然函数中的未知参数进行估计,然后按照似然比检验准则产生一个与噪声功率无关的检测统计量。通过数学推导和仿真实验都证明了该检测统计量在任何信道条件下都具有良好的抗噪声不确定性的优点。当存在噪声不确定性时,仿真结果还表明在接收数据长度和信噪比相同的条件下,本文GLRT算法的检测概率将明显高于能量检测法。
在第3章,首先对循环谱的基本理论、估计方法进行了阐述。然后介绍了Dandawate和Giannakis提出的循环谱统计量检测算法。最后从降低循环谱检测统计量构造复杂度、如何设置判决门限、提高检测概率的角度出发,提出了一种基于频域平滑的循环谱统计量(FSCSS)频谱检测算法,在对检测统计量的概率分布进行了理论分析的基础之上,通过数学推导得到了高斯白噪声信道和瑞利衰落信道下,检测统计量虚警概率和检测概率的解析表达式,并解决了判决门限的设置问题。随后把在一个循环频率处构造检测统计量的方法推广到在两个循环频率处构造检测统计量的情景中。仿真实验的结果表明:在接收数据长度和信噪比相同的条件下,本文FSCSS算法的检测概率将高于DG算法。
在第4章,由于多天线具有良好的抗衰落特性,所以可以将它与其他物理层检测方法相结合,从而达到进一步提高检测性能的目的。首先将基于频域平滑的循环谱统计量检测算法与多天线技术相结合,推导了采用等增益合并后检测统计量虚警概率的解析表达式,解决了判决门限的设置问题。然后给出了基于能量检测的多天线等增益合并法分别在两种信道条件下,虚警概率和检测概率的解析表达式。接下来介绍了Quan等人提出的最优线性加权合并频谱检测算法,并将其应用到多天线场景中。最后通过仿真实验将本文FSCSS算法的检测性能与这两种算法进行了比较。仿真结果表明:在接收数据长度和信噪比相同的条件下,本文FSCSS算法的检测概率最高。
另外,在第4章中还从降低循环谱算法计算量的角度出发,对基于频域平滑的循环谱统计量频谱检测算法提出一种折衷方案;然后从使感知用户的平均吞吐量最大化的角度出发,研究了多天线下的折衷循环谱统计量频谱检测算法检测时长的优化问题。通过数学推导和仿真实验都证明了最佳检测时长的存在性。仿真实验还给出了不同天线个数和不同参数条件下吞吐量性能的对比曲线,仿真结果表明:通过增加天线个数不仅可以提高感知用户的最大平均吞吐量,而且还缩短了最佳检测时长。
在第5章,为了抵抗深度衰落、提高频谱检测的可靠性,提出了一种新的基于集中式判决融合的合作频谱检测算法。该算法从使多感知用户合作检测概率最大化的角度出发,利用序列二次规划法求解控制中心的优化融合准则和各感知用户优化判决门限。本文首先给出了合作检测算法的数学描述。然后求解了不同信道条件下合作检测算法中联合判决概率的表达式。接下来分别对合作检测算法的优化问题、求解方法、实施步骤进行了阐述。最后对2比特软判决合作检测算法和两个感知用户判决相关条件下合作检测算法的优化问题进行了讨论。仿真实验得到以下结果:第一,无论是从检测概率随信噪比变化的曲线,还是从接收机操作特性曲线都可以看出,与其他融合准则固定的合作检测算法相比,本文的优化合作检测算法的检测概率始终是最高的。第二,在检测概率相同的条件下,通过多用户合作,可以缩短检测时长。第三,在信噪比相同的条件下,2比特软判决相比于1比特硬判决优化合作检测算法而言,检测概率只有微小改善,而且随着感知用户的增加,改善量越来越小。第四,两个相关感知用户的相关系数越大,与它们相互独立时相比,合作检测概率的改善量损失越大。
With the rapid development of wireless communication technologies, wireless service is now focusing on broadband multimedia business. However, the lack of wireless spectrum is becoming the obstacle of implementing these service. Dynamic spectrum access is considered an effective approach to solve shortness of wireless spectrum resources and lowness of spectrum utilization. Cognitive radio can dynamically reuse those idle authorized spectrum in temporal and spatial domain, and provide usable spectrum resources for new wireless applications. Therefore, cognitive radio is considered as an ideal carrier of implementing dynamic spectrum access in the future. As an key technology of cognitive radio physical layer, idle spectrum detection is the premise of cognitive radio system to operate normally. Starting from the request of quickness, exactness, and sensitiveness by spectrum detection, This dissertation studys idle spectrum detection deeply.
In chapter one, first, the particularity of spectrum detection in cognitive radio is discussed. Second, the spectrum detection technologies are classified in detail. Third, the merit and shortcoming of various spectrum detection methods has been summrized,and the outline of representative literature of each spectrum method is expatiated at the same time.
In chapter two, first, it is introduced the based concepts and rules of signal detection. Second, to overcome the serious shortcoming of failing to noise uncertainty by energy detection, it is proposed a blind spectrum detection algorithm based on generalized likehood ratio rule by this dissertation. First, the unknown parameters of likehood function are estimated through maximum likehood ratio rule under two hypothesis. Then, it is produced a detection statistic which is not ralated to nosie power by likehood ratio test rule. This statistic is free of noise uncertainty through mathematical deduction and computer simulation. When the noise uncertainty exists, if the length of received data and SNR are the same, the simulation results show that the detection probability of GLRT algorithm exceeds the energy detection clearly.
In chapter three, first, the basic theory and esitimation methods of cyclic spectral are expatiated. Second, it is introduced the cyclic spectral statistic detection algorithm brought forward by Dandawate and Giannakis. Starting from deducing cyclic spectral detection statistic constructing complexity,how to set the threshold and improving detection probability, an cyclic spectral statistic spectrum detection algorithm based on freqency domain smoothing(FSCSS) is proposed by this dissertation. First, the distribution of detection statistic is analyzed theoretically. Second, it is deduced analytic expression of the false alarm probability and detection probability under AWGN channel and flat Rayleigh fading channel, the threshold setting is solved at the same time. Then, the FSCSS algorithm which has construct detection statistic at one cyclic frequency is extended to construct detection statistic at two frequency. The simulaiton results show when the length of received data and SNR are the same, the detection probability of FSCSS algorithm exceeds the DG algorithm.
In chapter four, because of the merit of resisting fading, the multi-antenna technology can be combined with other physical layer detection methods, so as to further improve detection probability. First, the cyclic spectral statistic spectrum detection algorithm based on freqency domain smoothing is combined with multi-antenna technology, then it is deduced analytic expression of false alarm probability, and the threshold setting is solved at the same time. Second, analytic expression of the false alarm probability and detection probability of multi-antenna equal gain combining energy detection method are present. Third, it is introduced the optimal linear weighted combining detection algorithm proposed by Quan etc. At last, the three detection algorithms are compared through simulaiton, the results show that, when the length of received data and SNR are the same, the detection probability of the proposed algorithm is the best of all.
In addition, starting from deducing calculation of cyclic spectral detection algorithm, a compromising scheme is proposed. Next, starting from maximizing the average throughput of sensing user, the optimization of detection time of compromising multi-antenna cyclic spectral statistic spectrum detection algorithm is study. It is proved the optimal detection time is existent through mathematical deduction and computer simulation. The simulation experiments give the performance curve of throughput under different number of antennas and paremeters. The simulation results show that increasing the number of antennas can not only improve the maximum average throughput, but also shorten the optimal detection time.
In chapter five, to resist deep fading and improve reliability of spectrum detection, a new cooperative spectrum detection algorithm based on centralized decision fusion is proposed. The proposed algorithm starts from maximizing the global cooperative detection, solves optimized the fusion rule of control center and thresholds of each cognitive user by sequential quadratic programming. First, the mathematical description of cooperative detection algorithm is present. Second, solution of uniting judging probability are given under different channel. Third, the optimizing problem of cooperative detection algorithm, solution method, implementation steps are expatiated. At last, the soft decision optimal cooperative spectrum detection algorithm and optimal cooperative spectrum detection algorithm under correlated decision are discussed. The simulation experiments get results as followed: first, whether from the curve of detection probability versus SNR, or from receiver operating characteristic curve, compared with other cooperative detection algorithm whose fusion is fixed, the detection probability of the proposed algorithm is always the best. Second, when the detection probability is the same, the detection time is shorter by users cooperation.Third, when SNR is the same, compared with hard decision algorithm, the detection probability of soft decision algorithm has only a little improvement. And with the raise of sensing users, the improvement is worse and worse. Fouth, compared with two users independent, the bigger the correlated coefficient of two users is, the bigger the loss of improvement of cooperative detection probability is.
引文
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