A Spatio-Stochastic Framework for Cross-Layer Design in Cognitive Radio Networks

In this paper, we address the problem of distributed resource management for secondary users in Cognitive Radio Networks (CRNs), through a topology aware and frequency agile cross-layer approach. We exploit the theory of spatial processes and propose a Markov Random Field (MRF) based framework, which enables secondary CRN users to achieve efficient and viable mechanisms in the lower protocol stack layers by exchanging local only information. Specifically, through Gibbs sampling secondary users can optimize in a distributed and parallel manner their channel allocation, medium access and routing without resolving to otherwise computationally demanding optimization approaches. Through analysis and simulation we exhibit the efficacy of the proposed framework and show that a semi-parallel implementation can significantly reduce the required overhead cost compared to the sequential Gibbs sampling approach, while retaining a very close performance to the latter. We also study the emerging trade-offs by demonstrating the performance benefits in terms of channel assignment, medium access and data flow.