A DAM Spectrum Sensing Algorithm of Cognitive Radio Network Based Random Matrix

Weiting Gao, Fei Ma, Guobing Cheng, Weilun Liu
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Abstract

Aiming at the problem that the eigenvalue based spectrum sensing algorithms don't perform well in the situation of low SNR and small sample, based on the difference value between the maximum and minimum eigenvalue spectrum sensing algorithm (DMM), a Difference between the mean and minimum eigenvalue spectrum sensing algorithm (DAM) was proposed via the limiting distribution of minimum eigenvalue and the energy quality of the mean eigenvalue with the Random Matrix Theory (RMT). Analyze the algorithm performance (DAM1 and DAM2) with two different thresholds relatively, which were deducted in different ways. The simulation results show the DAM has the best performance without increasing algorithm complexity over the DMM and current Difference between the Maximum and average Energy spectrum sensing algorithm (ME-S-ED) in the situation of low SNR and sample, the DAM1 suits low SNR better and the DAM2 suits sample better.
基于随机矩阵的认知无线网络DAM频谱感知算法
针对基于特征值的频谱感知算法在低信噪比和小样本情况下性能不佳的问题,基于最大和最小特征值频谱感知算法(DMM)的差异值,利用随机矩阵理论(RMT),通过最小特征值的极限分布和平均特征值的能量质量,提出了一种基于均值和最小特征值的频谱感知算法(DAM)。相对分析两种不同阈值(DAM1和DAM2)的算法性能,并以不同的方式扣除。仿真结果表明,在不增加算法复杂度的情况下,DAM算法在低信噪比和低样本情况下的性能优于DMM算法和最大平均能谱感知算法(ME-S-ED),其中DAM1更适合低信噪比,DAM2更适合样本。
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