Robust Energy Efficiency Power Allocation Algorithm for Cognitive Radio Networks with Rate Constraints

Most of traditional power allocation algorithms in the cognitive radio networks (CRNs) are often based on the assumption of perfect channel estimation. We investigate the power allocation algorithm by considering channel gain uncertainty where a primary ad-hoc network working in parallel with a secondary ad-hoc network. Reducing interference and saving energy are essential in radio resource management of cognitive radio networks. The objective is to minimum transmit power while guaranteeing both acceptable transmission data rate for secondary users (SUs) and interference constraints for primary users (PUs). Imperfect channel state information is considered by ellipsoid sets and the problem can be formulated to a second-order cone programming problem. We can solve the robust power allocation problem by a distributed algorithm efficiently. Numerical results verify that the proposed algorithm with rate constraints can get higher transmission performance for SUs.