A study on SNR estimation for cognitive radio

In detect and avoid techniques of ultra-wide-band systems, there are some avoidance techniques which require information of both a primary signal and a noise level in order to control the transmitted power. Thus, we need to estimate the primary signal power and the noise variance with a high degree of accuracy. In this paper, we propose a new estimation method of primary signal to noise ratio. Our proposed method assumes several models of observed carriers, adopts the maximum likelihood criterion to estimate parameters in each model and selects the most suitable model based on the Akaike information criterion. By computer simulations, we evaluate the proposed estimator and show that the proposed method can estimate the parameters with a high degree of accuracy in several situations.