基于功率谱分段对消频谱感知算法研究及性能分析

在实用的认知无线电系统中，频谱感知技术必须具备在噪声电平高动态变化和无线信道严重衰落电磁背景下，进行实时盲频谱感知的能力，这为经典的频谱感知算法带来巨大的挑战。该文提出的功率谱分段对消频谱感知算法，依据傅里叶变换的渐进正态性和相互独立性，计算出功率谱的统计特性，利用监测频带内部分谱线强度和与全部谱线强度和的比值作为检验统计量进行信号存在性的判断。该文推导了算法的虚警概率和不同信道模型下正确检测概率的数学表达式，并依据Neyman-Pearson准则得到判决门限的闭式表达式。理论分析和仿真结果均表明：功率谱分段对消频谱感知算法对噪声不确定度具有鲁棒性；固定信噪比，算法的频谱感知性能不受噪声电平改变的影响；应用于高斯白噪声和平坦慢衰落信道中，可在较宽的信噪比范围内获得较优越的频谱感知性能；算法计算复杂度低，可在微秒级时长内完成频谱感知。

Research and Performance Analysis of Spectrum Sensing Algorithm Based on the Power Spectral Density Segment Cancellation

In a valid cognitive radio system, the requirement for real-time spectrum sensing in the case of lacking priori information of primary user, fading channel and dynamically varying noise level, indeed poses a major challenge to the classical spectrum sensing algorithms. In this paper, a novel spectrum sensing algorithm based on the Power spectral density Segment Cancellation (PSC) is proposed. It makes use of asymptotic normality and independence of Fourier transform to get the stochastic properties of Power Spectral Density (PSD). The proposed algorithm takes the ratio of some PSD lines to all of them as the detection statistics to detect signals. The mathematical expression for probabilities of false alarm and correct detection in different channel models is derived. In accordance with the Neyman-Pearson criteria, the closed-form expression of decision threshold is calculated. The theoretical analysis and simulation results show that the PSC algorithm is robust to noise uncertainty, and spectrum sensing performance does not vary with the ambient noise level of secondary users when Signal to Noise Ratio (SNR) is fixed. Meanwhile, the PSC algorithm could offer high probability of detection at low probability of false alarm for a wide range of the SNR in the white Gaussian noise and flat slow fading channel. The PSC spectrum sensing algorithm has low computational complexity, which can be completed in a micro-seconds duration.