Robust Resource Allocation in Full-Duplex Cognitive Radio Networks

Abstract

In this paper, we study resource allocation for secondary users (SUs) in underlay full-duplex cognitive networks, where the channel state information of the links between SUs and primary users (PUs) is uncertain. To protect the transmission of the PUs from interference generated by the SUs, we utilize robust optimization theory to characterize the channel uncertainty and formulate a resource allocation problem by jointly optimizing sub-channel assignment, user pairing, and power allocation. By using the dual method, we decompose the original resource allocation problem into a primal problem and a dual problem. We adopt the concave-convex procedure to transform the primal problem into a tractable form through sequential convex approximations while we utilize the sub-gradient method to solve the dual problem. Simulation results demonstrate the effectiveness of our proposed algorithm.