Interference efficiency: A new concept to analyze the performance of cognitive radio networks

In this paper, we develop and analyze a novel performance metric, called interference efficiency (IE), that shows the number of transmitted bits per unit of interference energy imposed on the primary users (PUs) in an underlay cognitive radio network (CRN). Specifically, we develop a framework to maximize the IE of a CRN with multiple secondary users (SUs) while satisfying target constraints on the average interference power on PU receiver, total SUs transmit power and minimum ergodic rate for the SUs. In doing so, we formulate a multiobjective optimization problem (MOP), that aims to achieve the maximum ergodic sum rate of multiple SUs and the minimum average interference power on the primary receiver. We show how the optimal point for the IE-maximization problem can be extracted from the Pareto optimal region of the proposed MOP. The MOP is solved by first transferrin g it into a single objective problem (SOP) using a weighted sum method. The formulated SOP is a nonconvex optimization problem and we solve it using an augmented Lagrange approach. The augmented Lagrangian which consists of a penalty-like quadratic terms changes the problem to become locally convex and eliminates the duality gap. Numerical results are conducted to corroborate our theoretical analysis.