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

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.