基于频域Katz分形维数的快速盲频谱感知

针对目前频谱感知方法对噪声不确定性敏感、低信噪比下准确度较差和运算速度慢的问题，提出了基于频域Katz分形维数的快速盲频谱感知方法。该方法根据信号和噪声的频域Katz分形维数特征的不同来感知主用户的存在。Monte Carlo仿真结果表明，该方法具有对噪声功率不敏感、受调制参数影响小、低信噪比下准确度高和运算速度快的优点，并能有效克服盒维数方法对某些调制参数的信号无法感知的缺点，可用于低信噪比、噪声具有不确定性情况下的快速盲频谱感知。

Fast blind spectrum sensing based on Katz fractal dimension in frequency domain

Current spectrum sensing methods are sensitive to noise uncertain, inaccurate in low signal to noise ratio (SNR) situations and low in calculation speed. To solve these issues, a fast blind spectrum sensing method based on Katz fractal dimension in frequency domain is proposed. It senses the presence of primary users according to different characteristics of Katz fractal dimension in frequency domain between signals and noise. Monte Carlo simulations show that the proposed method has some excellent advantages, such as insensitivity to noise uncertain, less influence from modulation parameters, high accuracy in low SNR situations, and high calculation speed. Furthermore, it can overcome the limitation of box dimension method which cannot detect the signals of some modulation parameters, so it can be used for fast blind spectrum sensing in situations of low SNR and uncertain noise.