基于主用户特性的频谱检测技术研究

发布时间：2018-03-17 14:02

本文选题：时域延续性　切入点：马尔可夫转移特性　出处：《华侨大学》2017年硕士论文　论文类型：学位论文

【摘要】：认知无线电是一种提高授权频段频谱利用率的新兴技术。频谱检测是其关键任务之一。本文分析主用户的行为特性,并研究基于该特性的频谱检测技术。本文首先介绍了认知无线电的背景、概念和关键技术,阐述了频谱检测技术。接着,对现有的几种检测技术进行分析和比较。其中,能量检测法因为结构简单、计算量小,得到广泛使用。其次,本文分析了主用户的时域延续性,介绍了两时隙时域延续性模型,并研究基于该模型的快速能量检测算法。该算法将前两次能量检测的检测结果当作主用户在前两个时隙的实际状态,然后基于时域延续性预判主用户的当前状态,从而减少检测次数,达到快速检测的目的。其中,状态预判可基于OR准则或者AND准则进行,分别对应OTPED算法和ATPED算法。本文在先验等概的条件下推导了这两种算法的检测精度,分析了它们的检测次数。和传统算法相比,上述算法可以在检测精度近似的条件下减少17~20%的检测次数。再次,本文介绍了主用户的马尔可夫转移特性,并基于该特性预测主用户的当前状态,从而动态调整判决门限,提高频谱检测精度。首先假定主用户前一时隙的实际状态已知,在恒虚警条件下,推导出检测概率的理论上界。然后,为了解决主用户实际状态未知的问题,利用检测结果近似实际状态,提出了马尔可夫恒虚警能量检测(MCFED)算法和改进的MCFED(IMCFED)算法。MCFED算法在高信噪比情况下具有很高的检测概率,但在低信噪比区域性能较差。IMCFED则始终优于传统的频谱检测算法。最后,为了兼顾虚警概率和漏检概率的影响,本文研究利用主用户马尔可夫转移特性减少贝叶斯代价。首先提出了马尔可夫贝叶斯能量检测(MBED)算法。其思想是将前一时隙的检测结果看作主用户的实际状态,并根据检测结果选择当前时隙的判决门限。该算法计算量小,信噪比较高时性能较好。为了克服MBED算法在低信噪比条件下检测精度较差的问题,本文又提出了改进的MBED(IMBED)算法。该算法根据预测概率对判决门限进行动态调整,能够进一步减少贝叶斯代价,且适用的信噪比区域更广。
[Abstract]:Cognitive radio is a new technology to improve spectrum efficiency of authorized frequency band. Spectrum detection is one of its key Ren Wuzhi. Firstly, this paper introduces the background, concept and key technology of cognitive radio, and expounds the spectrum detection technology. Then, it analyzes and compares several existing detection techniques. The energy detection method is widely used because of its simple structure and low computational cost. Secondly, the time-domain continuity of the primary user is analyzed, and the time-domain continuity model of two time slots is introduced. The fast energy detection algorithm based on this model is studied, which regards the first two energy detection results as the actual state of the primary user in the first two time slots, and then prejudges the current state of the primary user based on the continuity of time domain. In order to reduce the number of times of detection and achieve the purpose of fast detection, the state prediction can be based on OR criterion or AND criterion, corresponding to OTPED algorithm and ATPED algorithm respectively. In this paper, the detection accuracy of these two algorithms is deduced under the condition of priori probability. The detection times of these algorithms are analyzed. Compared with the traditional algorithms, these algorithms can reduce the detection times by 17% or 20% under the condition of approximate detection accuracy. Thirdly, this paper introduces the Markov transfer characteristics of the primary users. Based on this characteristic, the current state of the primary user is predicted, and the decision threshold is dynamically adjusted to improve the accuracy of the spectrum detection. Firstly, the actual state of the previous slot of the primary user is assumed to be known, and under the condition of constant false alarm, The theoretical upper bound of detection probability is derived. Then, in order to solve the problem that the actual state of the primary user is unknown, the detection result is used to approximate the actual state. Markov constant false alarm energy detection (MCFED) and modified MCFED.MCFED have high detection probability in the case of high signal-to-noise ratio (SNR), but the performance of IMCFED in low signal-to-noise ratio (SNR) region is lower than that of the traditional spectrum detection algorithm. Finally, the MCFED algorithm is always superior to the traditional spectrum detection algorithm. In order to balance the influence of false alarm probability and missed detection probability, In this paper, we study how to reduce the Bayesian cost by using the Markov transfer characteristic of the primary user. Firstly, we propose the Markov Bayesian energy detection (MBED) algorithm. The idea is that the detection results of the previous time slot are regarded as the actual state of the host user. According to the detection results, the decision threshold of the current time slot is selected. The algorithm has less computation and better performance when the signal-to-noise ratio is high. In order to overcome the problem of poor detection accuracy of MBED algorithm under low SNR, In this paper, an improved MBED-IMBED-based algorithm is proposed, which dynamically adjusts the decision threshold according to the prediction probability, which can further reduce the Bayesian cost and has a wider range of applicable signal-to-noise ratio (SNR).
【学位授予单位】：华侨大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN925

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