Cognitive model selections in co-existing operation of wireless sensor networks

In this paper, we describe the cognitive radios sharing the spectrum with licensed users and its effects on operational coexistence with unlicensed users. Due to the unlicensed spectrum band growing needs and usage by many IEEE 802.11 protocols, normal wireless radio operation sees high interference leading to high error rates on operational environments. We study the licensed bands and the characteristics of the unlicensed bands, as it is know that the licensed bands have a maximum limit of FCC interference for a licensed set of frequencies. The cognitive algorithm for this probabilistic model for the unlicensed users, uses a model which takes into account the threshold variable ratio Eb/No and also calculates the lower-bound of the combined value of secondary user interference for overlapping frequencies with the primary user. By using simulation, we detect the primary user when the radio frequencies are known a priori and compare it when the frequencies are unknown and needs to cognitively detected. In our analysis we exploit the similarity measure seen at each sub-channel frequencies, which are due to multiple paths of the same reflected signal by maximizing the correlated information of the correlation matrix. For the general case the covariance matrix for blind source separation, we use ICA de-correlation methods and show that cognitive radios can efficiently identify users in complex situations.