A relative rate utility based distributed power allocation algorithm for Cognitive Radio Networks

In an underlay Cognitive Radio Network, multiple secondary users coexist geographically and spectrally with multiple primary users under a constraint on the maximum received interference power at the primary receivers. Given such a setting, one may ask “how to achieve maximum utility benefit at the secondary users given the imposed interference temperature constraint”? In an attempt at answering this question, we introduce a measure of the marginal secondary utility per unit primary interference (termed as relative rate utility) and propose a distributed algorithm that tries to maximize this measure. We present selected computer based Monte-Carlo simulation results, demonstrating the effectiveness of the introduced measure, and the improved performance of the proposed algorithm.