probabilistic spectrum access function

Joint optimization of spectrum sensing and spectrum access parameters of a cognitive radio sensor network (CRSN) leads to a higher sum throughput of secondary users (SUs) while the interference introduced to primary users (PUs) is kept under certain tolerable level. In this work, first, by using the concept of probabilistic spectrum access, joint spectrum sensing and power allocation is performed in a multiband CRSN. The considered optimization problem is formulated with the aim of maximizing the average opportunistic secondary data rate under constraints on the interference introduced to PU and limited power budget of SU. The considered system model leads to a non-convex optimization problem which is converted into a convex problem. Based on using genetic algorithms, optimal solution of this problem is obtained using two different approaches: i) Lagrange multipliers method and ii) Linear programming method. We provide several numerical simulation results to evaluate the performance of our proposed methods in terms of achievable CR data rate, interference introduced to the PU and convergence properties of the proposed algorithms.