基于变步长序贯压缩的频谱快速感知算法

对信号稀疏度未知甚至时变的频谱感知应用场景，将自适应思想与序贯压缩频谱感知技术相结合，提出了一种可变步长序贯压缩频谱快速感知算法。新算法建立了下一次判决所需观测值数目步长因子与当前对数似然比距门限距离之间的函数关系，克服了现有的序贯压缩检测算法以固定步长增加观测值的不足。分别以分段函数和抛物线函数为步长因子调整规则进行理论分析和仿真实验。仿真结果表明：与现有的序贯压缩检测算法相比，变步长算法具有检测速度快、观测值数目少和计算复杂度小等优点。

Spectrum Sensing Algorithm Based on Variable Step Size Sequential Compressed Sampling

The signal sparsity is often unknown, or even changed with time in spectrum sensing. Therefore, a variable step size sequential compressed detection algorithm is proposed by combining the adaptive theory with the sequential compressed spectrum sensing technology. The functional relationship is established between step size factor of the next needed measurement numbers and current distance from the likelihood ratio and the detection thresholds. In addition, the shortcoming of the fixed step size of the measurement increment in the existing sequential compressed sensing is overcomed in the proposed algorithm. Theoretical analysis and computer simulations are conducted by introducing the rules of step size adjustment with piecewise function and parabolic function, respectively. Simulations prove that, the proposed algorithm has the faster detection speed, less measurements number and lower computational complexities, compared with the existing sequential compressed detection scheme.