Probabilistic vs. Sequence-Based Rendezvous in Channel-Hopping Cognitive Networks

Abstract

Rendezvous in cognitive networks refers to the ability of cognitive nodes to find each other and form a network, or to find and join an already operating cognitive network. Two main approached to rendezvous have emerged: sequence-based mechanism that guarantees maximum time-to-rendezvous and blind random hopping resilient to unpredictable primary user activity. In this paper we develop analytical models for time to rendezvous in the presence of primary user activity for the orthogonal sequence-based mechanism and a blind rendezvous mechanism integrated with a transmission tax-based MAC protocol with cooperative sensing. Our analysis shows that the blind mechanism performs better under random primary user activity, the difference being more pronounced when the number of channels is high and/or primary user activity is more intense. In addition, the probabilistic mechanism allows rendezvous with either an emergent or a fully operational CH-CPAN piconet without any interruption, unlike the sequence-based mechanism which precludes any data exchange during the rendezvous process.