基于截断型序贯检测的协作频谱感知算法

本文针对低信噪比情况下认知用户的频谱感知需求，提出了一种基于截断型序贯检测的协作频谱感知算法。协作认知用户对接收到的信号序列进行分段处理，统计各自段内采样点能量值大于预设门限值的个数并将其作为本地检测统计量上传至融合中心，有效的减少了控制信道的开销。棣莫弗—拉普拉斯定理和中心极限定理的引入，又使得段内检测统计量近似为高斯分布，从而大大简化了后续似然比函数的理论推导和计算过程。性能分析及仿真结果表明，在目标检测性能相同的条件下，相比现有的能量检测算法，所提算法大大降低了所需的频谱感知时间。

A Cooperative Spectrum Sensing Algorithm Based on Truncated sequential detection

Aiming at the spectrum sensing demand under low SNR environment of cognitive radio (CR) users, a cooperative spectrum sensing algorithm based on truncated sequential probability radio test (SPRT) is proposed. Each cooperative radio user counts the number of sampling points, while its energy value is greater than the preset threshold in every field by sectionally processing the signal sequences they received, and then upload them to the fusion center as local test statistics, which reduces the overhead of control channel effectively. By introducing De Moivre-Laplace theorem and central limit theorem, the local test statistics of intrasegment approximate Gaussian distribution, which greatly simplifies the theoretical deduction and calculative process of likehood ratio function subsequently. Performance analysis and simulation results show that under the same conditions of detection performance, the proposed algorithm reduces the spectrum sensing time largely compared with the conditional energy detection.