A Markov Random Field framework for channel assignment in Cognitive Radio networks

In order to alleviate the absence of hierarchical coordination among secondary users of Cognitive Radio networks and to enable fast and efficient reconfiguration of pervasive ad hoc networks, spatially localized and frequency agile mechanisms are required across the wireless protocol stack. In this paper, we introduce a framework for distributed and adaptable radio channel allocation in wireless Cognitive Radio networks, which is based on the theory of Markov Random Fields and Gibbs sampling. By exchanging local only information, secondary users are able to assign efficiently available channels according to desired operational requirements, even if these requirements change over time. Through analysis and simulation we show the applicability of the framework and demonstrate its distributed operation. We study the emerging trade-offs of the proposed approach by demonstrating the performance benefits obtained in radio channel allocation and by analyzing the inherent costs.