基于最小描述长度准则的稀疏多带信号频谱感知算法
doi: 10.3724/SP.J.1146.2011.01324
Spectrum Sensing Algorithm Based on Minimum Description Length Criterion for Sparse Multiband Signal
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摘要: 该文研究稀疏多带信号的频谱感知问题。首先利用欠采样的数据构造自相关矩阵,并对该矩阵进行特征值分解,然后根据最小描述长度(MDL)准则对特征值进行计算,利用结果区分信号子空间和噪声子空间,最后根据子空间的结果求出信号的频率支集,由此提出了一种针对稀疏多带信号的频谱感知算法。由于传统算法需要预先设置门限来区分信号子空间和噪声子空间,所以不合理的门限值会导致算法失效,该文算法不用预先设置门限,具有更好的适应性。仿真实验结果验证了该文算法的有效性。Abstract: Spectrum sensing issue for sparse multiband signals is considered in this paper. Firstly, undersampling at sub-Nyquist rate are used to construct correlation matrix, and eigenvalue decomposition is carried out. Then, the method uses Minimum Description Length (MDL) criterion to distinguish between signal subspace and noise subspace and finds the spectral support. A spectrum sensing algorithm for sparse multiband signal is presented. Traditional methods distinguish between signal subspace and noise subspace by setting threshold, and make mistake when the threshold is wrong. Adaptability of this algorithm is better because there is no need to set threshold. The simulation results verify the validity of this algorithm.
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