Dynamic spectrum allocation in cognitive radio using hidden Markov models: Poisson distributed case

Abstract

Cognitive radio networks can be designed to manage the radio spectrum more efficiently by utilizing the spectrum holes in primary users' licensed frequency bands. Recent studies have shown that the radio spectrum is poorly utilized by the licensed users even in urban geographical areas. This spectrum utilization can be improved significantly by making it possible for secondary users (who are not being served by the primary system) to access spectrum holes, i.e., frequency bands not used by licensed users. In this novel work, we use hidden Markov models (HMMs) to model and predict the spectrum occupancy of licensed radio bands. The proposed technique can dynamically select different licensed bands for its own use with significantly less interference from and to the licensed users. It is found that by predicting the duration of spectrum holes of primary users, the CR can utilize them more efficiently by leaving the band, that it currently occupies, before the start of traffic from the primary user of that band. We propose a simple algorithm, called the Markov-based channel prediction algorithm (MCPA), for dynamic spectrum allocation in cognitive radio networks. In this work, we present the performance of our proposed dynamic spectrum allocation algorithm when the channel state occupancy of primary users are assumed to be Poisson distributed. The impact of CR transmission on the licensed users is also presented. It is shown that significant SIR improvements can be achieved using HMM based dynamic spectrum allocation as compared to the traditional CSMA based approach. The results obtained using HMM are very promising and HMM can offer a new paradigm for predicting channel behavior in cognitive radio, an area that has been of much research interest lately.