Robust Worst-Case Interference Control in Underlay Cognitive Radio Networks

Abstract

We investigate the problem of power allocation to secondary users (SUs) in underlay cognitive radio networks (CRNs), where the channel state information (CSI) pertaining to the link between an SU's transmitter and a primary user (PU) receiver is uncertain. To keep the interference of SUs to PUs below a given threshold under any realization of uncertainty in CSI, we utilize the robust optimization theory where uncertainty in CSI is defined by a bounded distance between its estimated and exact values, demonstrate that the convexity of allocating power to SUs is preserved, and show that it can be solved in a computationally efficient manner. Considering worst-case interference is a very conservative approach that, although protects PUs to the maximum extent, may cause undesirable and significant reductions in the SUs' throughput as well, which at many instances may not be necessary. Hence, when permissible, it is worthwhile to tradeoff between the robust worst-case interference control to PUs and the SUs' throughput. In doing so, we maintain the probability of violating the interference to PUs below a given threshold by applying the differential norm and the chance constrained approaches, both of which can be solved very efficiently via a water-filling-like formula. Simulation results show the effectiveness of our proposed approach.