Eigenvalue ratio based blind spectrum sensing algorithm for multiband cognitive radios with relatively small samples

The multiband detection problem in relatively small sample scenarios where the number of subbands is comparable to the number of samples in magnitude is described. Combined with the sequential hypothesis testing, an eigenvalue ratio based method is proposed for the multiband spectrum sensing (MSS). As the distribution of the new statistic when only noise is present can be precisely obtained by using the random matrix theory, the proposed method is able to reliably set the theoretical threshold, and outperforms the traditional MSS methods based on information theoretic criteria and principal component analysis, especially in the cases with small samples. Meanwhile, the proposed method belongs to a blind detection scheme and can be used in cases without prior knowledge of the primary signal, the channel and the noise. Simulation results show the superiority of the proposed method.