基于功率谱分段对消频谱感知算法研究及性能分析

发布时间：2018-05-04 10:47

本文选题：认知无线电 + 频谱感知　；参考：《电子与信息学报》2014年04期

【摘要】：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。
[Abstract]:In a practical cognitive radio system, spectrum sensing technology must have the ability of real-time blind spectrum sensing under the background of high dynamic change of noise level and serious fading electromagnetic background of wireless channel. This brings great challenge to the classical spectrum sensing algorithm. Based on the asymptotic normality and mutual independence of Fourier transform, the statistical properties of power spectrum are calculated. The signal existence is judged by using the ratio of the partial spectral line intensity and the sum of the total spectral line strength to the total spectral line strength in the monitoring frequency band as the test statistics. In this paper, the mathematical expressions of false alarm probability and correct detection probability under different channel models are derived, and the closed expression of the decision threshold is obtained according to the Neyman-Pearson criterion. The theoretical analysis and simulation results show that the power spectrum segmented de-spectrum sensing algorithm is robust to noise uncertainty, and the spectrum sensing performance of the algorithm is not affected by the change of the noise level, and the fixed signal-to-noise ratio (SNR) is not affected by the noise level. When applied to Gao Si white noise and flat slow fading channels, the spectrum sensing performance can be obtained in a wide range of signal-to-noise ratio (SNR). The algorithm has low computational complexity and can complete spectrum sensing in microsecond order.
【作者单位】：西安电子科技大学综合业务网国家重点实验室;
【基金】：国家新一代宽带无线移动通信网科技重大专项(2010ZX03006-002-04) 国家自然科学基金(61072070) 教育部博士学科点基金(20110203110011)资助课题
【分类号】：TN925

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