认知无线电系统中若干关键技术的研究
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摘要
随着无线通信技术的飞速发展,可供使用的频谱资源越来越匮乏。为了解决频谱资源不足的问题,采用机会方式频谱接入机制的认知无线电技术得到了人们的重视。认知无线电技术从根本上颠覆了传统的固定频谱分配方式,它可以感知周围环境的变化,并通过改变传输功率、调制方式、载波频率等传输参数适应这种变化,从而提高频谱资源的利用率。频谱分配问题是认知无线电系统中一个关键问题,它对可使用的频谱资源进行分配,以保证较高的频谱利用率。为了检测频谱资源是否可以由认知无线电系统使用,需要使用频谱感知算法来检测频谱,如果主用户出现在由次用户占用的频段中,次用户必须及时切换到其他频段,或者保证对主用户的干扰小于临界值。为了保证次用户对主权用户的干扰小于临界值,认知无线电系统需要检测主用户接收机附近的干扰功率,因此需要对主用户进行定位。
本文对认知无线电系统中的频谱分配、频谱感知和主用户定位问题进行了深入研究,主要贡献如下：
(1)针对认知无线电系统中的频谱分配问题,提出了一种固定速率认知用户和变速率认知用户并存情况下的动态频谱分配算法。该算法以最小化每比特发射功率为目标,应用最小最大优化准则与分步求解的方法,对系统目标函数予以简化,在最大公平意义上对认知小区的空闲频谱资源进行动态分配。
(2)针对在感知周期中次用户不能发射信号而导致的资源利用率降低的问题,提出了基于循环平稳信号分析的频谱感知算法。首先,根据次用户的循环平稳特征估计出次用户和主用户的接收功率,然后通过将主用户的接收功率和判决门限进行比较来判定主用户是否存在。该算法可以在主用户与次用户同时占用信道的情况下感知主用户的存在,从而提高了频谱利用率。
(3)针对认知无线电系统的频谱感知问题,提出了一种联合能量相关检测算法。利用大部分接收信号具有相关性的特点,综合考虑接收信号的能量和自相关函数,推导出了联合概率密度函数,并得到了联合能量相关检测器的假设检验公式。
(4)基于随机集的高斯混合概率假设密度滤波算法是一种典型的多目标跟踪算法,它可以在目标数目和出现未知的情况下进行多目标跟踪,本文提出了一种改进的高斯混合概率假设密度滤波算法,并将该算法应用于认知无线电系统的主用户跟踪问题。该算法利用双向预测的方式对检测结果进行估计,即使用正向预测算法来估计现存主用户的位置,然后采用后向预测算法来搜索新生的主用户并估计出新生主用户的位置。本文提出的算法在主用户的数目、出现的时间和起始位置均未知的情况下,仍可以有效地跟踪主用户。
With the development of wireless communications technology, the spectrum resources are increasingly scarce. In order to solve the problem of insufficient spectrum resources, the cognitive radio technology based on the opportunity spectrum access mechanism plays a more important role. Cognitive radio technology subverts the traditional fixed spectrum allocation fundamentally. It can sense changes of the environment by the sensing node, and change the transmission power, modulation, carrier frequency and other transmission parameters to improve the efficiency of spectrum. Spectrum allocation is a key technology in cognitive radio systems. It can allocate the free spectrum resources and ensure a higher spectrum efficiency. The spectrum sensing technology is used to make sure that the spectrum is not used by the primary user. The cognitive radio system must detect the interference power nearby the primary user's receiver to ensure the interference power from the secondary user is less than a threshold. So the cognitive radio system must know the location information of the primary user.
In this dissertation, we research on the spectrum allocation, spectrum sensing and primary user tracking problems in the cognitive radio system. The main contributions of the dissertation are as follows:
(1) A new spectrum allocation algorithm for cognitive radio is proposed based on the analysis of the OFDM based cognitive radio system. For this system that the constant rate users and the variable rate users are coexistence with each other, the proposed algorithm is minimizing the transmitted power per bit, allocating the free spectrum in the cognitive radio cell dynamically in a sense of fair maximization. The system object function is simplified by using the min-max criterion and the step by step approach.
(2) A cyclo-energy detector is proposed for the spectrum sensing problem based on the cyclostationary signal analysis. It can determine whether there exists primary user by estimating the primary user's received power according to the cyclostationarity of the secondary user. By using this detector, the secondary user need not stop transmitting when sensing the frequency spectrum.
(3) By using the features which most of the received signals are correlated, a joint energy and autocorrelation based spectrum sensing algorithm is proposed. The hypothesis testing formula has been derived, and the detection probability of the primary user has been improved.
(4) The primary user location problem is a key issue of the cognitive radio system. Locating the primary user's accurate position can provide a better service for the users. The random set based Gaussian mixture probability hypothesis density filter algorithm is a typical multi-target tracking algorithm. It can track multiple targets when the number of the targets is unknown. In this dissertation, a modified Gaussian mixture probability hypothesis density filter is developed for multi-target tracking problem because the traditional Gaussian mixture probability hypothesis density filter can not work well when where the targets will appear is unknown. And the proposed algorithm is applied to track the primary users in the cognitive radio systems. A double side prediction algorithm is adopted to solve this primary user tracking problem. First, the forward prediction algorithm is used to estimate the locations of the existed primary users, and then the backward prediction algorithm is used to search the new primary users. The proposed algorithm can be used when we do not know how many primary users exist, and when and where they will appear. Simulation results show that the proposed algorithm can track the primary users even in a high false detection environment.
本文对认知无线电系统中的频谱分配、频谱感知和主用户定位问题进行了深入研究,主要贡献如下：
(1)针对认知无线电系统中的频谱分配问题,提出了一种固定速率认知用户和变速率认知用户并存情况下的动态频谱分配算法。该算法以最小化每比特发射功率为目标,应用最小最大优化准则与分步求解的方法,对系统目标函数予以简化,在最大公平意义上对认知小区的空闲频谱资源进行动态分配。
(2)针对在感知周期中次用户不能发射信号而导致的资源利用率降低的问题,提出了基于循环平稳信号分析的频谱感知算法。首先,根据次用户的循环平稳特征估计出次用户和主用户的接收功率,然后通过将主用户的接收功率和判决门限进行比较来判定主用户是否存在。该算法可以在主用户与次用户同时占用信道的情况下感知主用户的存在,从而提高了频谱利用率。
(3)针对认知无线电系统的频谱感知问题,提出了一种联合能量相关检测算法。利用大部分接收信号具有相关性的特点,综合考虑接收信号的能量和自相关函数,推导出了联合概率密度函数,并得到了联合能量相关检测器的假设检验公式。
(4)基于随机集的高斯混合概率假设密度滤波算法是一种典型的多目标跟踪算法,它可以在目标数目和出现未知的情况下进行多目标跟踪,本文提出了一种改进的高斯混合概率假设密度滤波算法,并将该算法应用于认知无线电系统的主用户跟踪问题。该算法利用双向预测的方式对检测结果进行估计,即使用正向预测算法来估计现存主用户的位置,然后采用后向预测算法来搜索新生的主用户并估计出新生主用户的位置。本文提出的算法在主用户的数目、出现的时间和起始位置均未知的情况下,仍可以有效地跟踪主用户。
With the development of wireless communications technology, the spectrum resources are increasingly scarce. In order to solve the problem of insufficient spectrum resources, the cognitive radio technology based on the opportunity spectrum access mechanism plays a more important role. Cognitive radio technology subverts the traditional fixed spectrum allocation fundamentally. It can sense changes of the environment by the sensing node, and change the transmission power, modulation, carrier frequency and other transmission parameters to improve the efficiency of spectrum. Spectrum allocation is a key technology in cognitive radio systems. It can allocate the free spectrum resources and ensure a higher spectrum efficiency. The spectrum sensing technology is used to make sure that the spectrum is not used by the primary user. The cognitive radio system must detect the interference power nearby the primary user's receiver to ensure the interference power from the secondary user is less than a threshold. So the cognitive radio system must know the location information of the primary user.
In this dissertation, we research on the spectrum allocation, spectrum sensing and primary user tracking problems in the cognitive radio system. The main contributions of the dissertation are as follows:
(1) A new spectrum allocation algorithm for cognitive radio is proposed based on the analysis of the OFDM based cognitive radio system. For this system that the constant rate users and the variable rate users are coexistence with each other, the proposed algorithm is minimizing the transmitted power per bit, allocating the free spectrum in the cognitive radio cell dynamically in a sense of fair maximization. The system object function is simplified by using the min-max criterion and the step by step approach.
(2) A cyclo-energy detector is proposed for the spectrum sensing problem based on the cyclostationary signal analysis. It can determine whether there exists primary user by estimating the primary user's received power according to the cyclostationarity of the secondary user. By using this detector, the secondary user need not stop transmitting when sensing the frequency spectrum.
(3) By using the features which most of the received signals are correlated, a joint energy and autocorrelation based spectrum sensing algorithm is proposed. The hypothesis testing formula has been derived, and the detection probability of the primary user has been improved.
(4) The primary user location problem is a key issue of the cognitive radio system. Locating the primary user's accurate position can provide a better service for the users. The random set based Gaussian mixture probability hypothesis density filter algorithm is a typical multi-target tracking algorithm. It can track multiple targets when the number of the targets is unknown. In this dissertation, a modified Gaussian mixture probability hypothesis density filter is developed for multi-target tracking problem because the traditional Gaussian mixture probability hypothesis density filter can not work well when where the targets will appear is unknown. And the proposed algorithm is applied to track the primary users in the cognitive radio systems. A double side prediction algorithm is adopted to solve this primary user tracking problem. First, the forward prediction algorithm is used to estimate the locations of the existed primary users, and then the backward prediction algorithm is used to search the new primary users. The proposed algorithm can be used when we do not know how many primary users exist, and when and where they will appear. Simulation results show that the proposed algorithm can track the primary users even in a high false detection environment.
引文
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