Uplink modeling of cognitive radio network using stochastic geometry

In this study, we investigate the coverage probability and spectral efficiency of a cognitive radio network with a single-tier uplink model based on a stochastic geometry framework. The locations of cognitive transmitters, cognitive receivers, primary transmitters, and primary receivers are modeled as independent Poisson point processes. We derive the mathematical expressions for the coverage probability and spectral efficiency of this network, as well as closed-form expressions for a path-loss exponent equal to 4. For the uplink transmission, truncated channel inversion power control is employed for the cognitive transmitter with a cutoff threshold at the cognitive receiver because of the limited transmission power. We consider two locations of the cognitive receiver (i.e., inside and outside the primary exclusion region) for uplink transmission. We found that the location of the cognitive receiver impacts on coverage probability and spectral efficiency. Numerical analysis confirms that (i) the coverage probability is higher and (ii) the spectral efficiency is higher when the cognitive receiver is outside the primary exclusion region.