Sensor Selection Under Correlated Shadowing in Cognitive Radio Networks

In this letter, we study optimal sensor selection for cooperative sensing in cognitive radio networks using a linear soft fusion approach. Our goal is to find a set of K sensors and its optimal linear combination rule that maximizes the expected system capacity of secondary users while meeting the requirement on the protection of primary transmission. We formulate the problem as a cardinality-constrained convex optimization problem and propose approximation algorithms that account for the effect of pathloss and correlated shadowing. Simulation results show that the proposed algorithms achieve comparable performance to the optimal solution.