Dynamic Markov-Chain Monte Carlo Channel Negotiation for Cognitive Radio

In ad hoc cognitive radio (CR) networks, channel negotiation and access have been raised as challenging issues due to its highly dynamic nature and strong user diversity, particularly in situations where a dedicated common control channel is not reserved among the distributed CR nodes. In this paper, a novel stochastic channel negotiation algorithm is proposed for improving spectrum sharing efficiency in the CR networks. The paper first formulates the problem of channel selection for negotiation, aiming to maximize the probability of successful channel negotiation. The formulated optimization problem is then solved using a dynamic Markov-Chain Monte-Carlo (MCMC) scheme. Simulation is conducted to examine the performance of the proposed approach and demonstrate its merits. We have witnessed that the proposed approach can serve as an excellent complementary to the CR networks in which dedicated control channels are not defined.