Improved spectrum sensing algorithms based on eigenvalue ratio of random matrix

Aiming at the shortcomings of the existing spectrum sensing methods in cognitive radio, two improved algorithms based on the eigenvalue ratio of random matrix are proposed through using the property of asymptotic spectrum of random matrix by means of random matrix theory and researching distribution of average eigenvalue of the covariance matrix of the received signals. Improved algorithms not only need neither the prior knowledge of primary signal, nor the power of background noise, but also have quite simple closed form expressions. Simulation results show that the improved algorithms can get a good performance even under the situation of few samples, few collaborative users, low signal to noise ratio and low false alarm probability.