基于信噪比估计的自适应频谱感知算法

针对传统能量检测不能对低信噪比条件下的信号进行准确感知，容易造成误判的缺点。为了提高在低信噪比条件下的频谱感知性能并缩短感知时间，结合循环特征检测具有较高的检测性能和鲁棒性但计算复杂度高的特点，提出了基于信噪比预估计的自适应频谱感知算法。该算法通过预估计待检信号与信道噪声的信噪比，当高于信噪比选择阈值时，采用改进后的自适应门限能量检测，降低运算复杂度；若低于选择阈值则进行循环特征检测，保证良好的检测精度；并可以根据系统对检测精度和感知速率的要求，自适应调整选择阈值的大小。仿真结果表明，所提算法有效的提高了低信噪比条件下频谱感知的准确性，缩短了平均感知时间。

Adaptive spectrum sensing algorithm based on SNR estimation

Considering of the traditional energy detection cannot accurately perceive the signal under the condition of low signal to noise ratio (SNR). It can easily lead to misjudgment. In order to improve the performance of spectrum sensing in low SNR conditions and shorten the sensing time, combined the cyclic feature detection has high detection performance and robustness but with high computational complexity. This paper developed an adaptive spectrum sensing algorithm based on SNR estimation. It calculated the pre estimation ratio of detection signal to noise signal. When the SNR is higher than the threshold value, the improved adaptive energy detection was performed to reduce the computational complexity, otherwise selected cyclic feature detection to ensure good accuracy. It could according to the requirements of the detection accuracy and efficiency of the sensing system adjusted the size of the selection threshold adaptively. Simulation experiments show that proposed algorithm can effectively improve the accuracy of spectrum sensing under low SNR and shorten the average detection time.