基于加权MUSIC的盲空间频谱感知算法

A Blind Spatial Spectrum Sensing Algorithm Based On Weighted Music

  • 摘要: 目前已存在的频谱感知算法只开发了时间、频率和地理维度,而角度维频谱资源的探索,即空间频谱感知尚不成熟。在本论文中,我们将多重信号分类(Multiple Signal Classification, MUSIC)这一到达角(Angle Of Arrival, AOA)估计算法应用到频谱感知场景中。注意到MUSIC算法需要知道信源信号的数目,但是在多数感知场景中达不到这一要求。因此,我们利用特征值加权方案设计一种无需估计信源数目的加权MUSIC到达角估计算法。最后利用该算法的最大最小谱值比,我们提出了一种基于加权MUSIC算法的盲频谱感知算法。新算法保持了MUSIC算法的高峰值和高分辨率的特点,在达到较高检测概率的同时可以为频谱接入提供AOA信息,进一步提高了频谱利用率。仿真结果证明了所提算法的有效性。

     

    Abstract: State-of-the-art sensing methods only exploit three dimensions of the spectrum space: frequency, time and geography whereas the angle dimension, that is, spatial spectrum sensing has not been exploited well enough. In this paper, we apply the multiple signal classification (MUSIC) Angle of Arrival (AoA) estimation method into spectrum sensing. Note that MUSIC method needs to know the number of signals, which is not available in sensing scenario. Hence, we use eigenvalue weighting scheme to design an improved MUSIC method which does not need the number of arriving signals. Utilizing the maximum-minimum spectrum ratio of the improved MUSIC method, we finally propose a blind weighted MUSIC based detection algorithm. Taking the advantage of the high peak and resolution of MUSIC method, the proposed method can achieve high probability of detection as well as offer the AOA information for spectrum access, which improve the spectrum efficiency. Simulation results are presented to verify the efficiency of the proposed algorithm.

     

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