【作者】 刘欣；

【导师】 李赞；

【作者基本信息】 西安电子科技大学 ， 军事通信学， 2011， 硕士

【摘要】 认知无线电技术作为一种智能无线通信系统,能够通过感知周围环境的变化,有效地利用空闲的频谱资源,成为解决无线频谱资源匮乏以及频谱利用率低下问题的关键技术。而认知无线电中的频谱感知技术是认知无线电系统能够正常工作的前提。本文从快速、准确的检测要求出发,针对无线区域网络的频谱感知技术展开了深入研究。本文首先介绍了几种经典的频谱感知技术,并讨论了噪声方差不确定时对现有检测算法的影响。为克服噪声不确定度对频谱检测性能的影响,本文研究了认知无线电中的基于熵的频谱感知问题,将非均匀量化思想引入熵检测理论中,提出了非均匀量化谱熵的频谱检测方法。该方法将接收频谱序列进行非均匀量化,使接收信号在仅含噪声时能够最大化熵值,从而能够提高检测性能,仿真结果表明相同条件下,所提方法相比于均匀量化谱熵检测能够获得约3dB的检测性能增益;并且所提算法的门限确定不受噪声方差的影响,因此系统性能具有噪声不确定度鲁棒性,在噪声方差不确定的条件下具有比能量检测更好的检测性能。本文还针对单节点检测所遇到的衰落、隐藏终端等问题,提出了基于非均匀量化谱熵的硬判决和软判决联合检测方法。联合检测能够融合多个节点的检测信息进行综合判决,比单节点检测具有更好的检测性能。本文对各个节点在相同信噪比下和不同信噪比下的合作检测算法进行仿真分析。仿真结果表明在相同信噪比条件下“K秩”融合性能最好,在信噪比为-10dB、虚警概率为0.2的条件下比单节点检测提高50%的检测概率。而在不同信噪比条件下,软判决中的最大比合并算法给不同信噪比节点分配不同权值,能够获得最好的检测性能,仿真结果表明该算法比“K秩”融合准则算法在虚警概率0.1的条件下提高约10%的检测性能。更多还原

【Abstract】 By exploiting the existing wireless spectrum opportunistically, cognitive radio technology is developed to solve current wireless network problems resulting from the limited available spectrum and the inefficiency in spectrum usage. A cognitive radio (CR) is designed to be aware of and sensitive to the changes in its surrounding, which makes spectrum sensing an important requirement for the realization of cognitive radio networks. Spectrum sensing enables CR users to detect spectrum holes without causing interference to the primary networks. This dissertation will make a deep study of the spectrum detection in wireless regional area networks.Firstly, several techniques of spectrum sensing are discussed and the concept of noise uncertainty is introduced. A spectrum sensing algorithm based on information theory in CR is considered to avoid the influence by noise uncertainty. By introducing the nonuniform quantization, the spectrum entropy-based detection scheme is proposed. The proposed scheme can improve quantization performance and maximize entropy value so as to improve the detective performance. The simulation results show that the scheme can obtain approximate 3dB gain in detection performance than spectrum entropy-based detection using uniform quantization. Furthermore, the simulation results verify the robustness against noise uncertainty, and show that the proposed scheme outperforms energy detection under noise uncertainty situation.Secondly, to the fading or hidden-station problems existed in single node detection, several hard decision methods and soft decision methods based on entropy detection in cooperative spectrum sensing are proposed in the dissertation. Fusion rules of cooperative spectrum sensing are compared by computer simulation under each node with same Signal to Noise Ratio (SNR) and different SNR. Simulation results show K out of N rule obtains the best performance under same SNR condition. And it increases detection probability by 50% than single node detection for a target false-alarm probability of 0.1 and SNR of -10dB. While maximal ratio combination (MRC) rule of the soft decision is the optimal algorithm under different SNR conditions. The simulation results show that MRC rule improves detective performance by 10% than K out of N rule for a target false-alarm probability of 0.1.更多还原