In cognitive radio networks (CRNs), the rendezvous problem—which entails creating a shared control channel among secondary users (SUs) in a distributed manner— continues to be a major challenge. In highly dynamic spectrum situations, traditional channel-hopping and blind rendezvous algorithms frequently have inefficiency, scalability problems, and excessive latency (Time-to-Rendezvous, or TTR).  This paper proposes a novel, joint sensing and rendezvous for efficient sharing of common channel within the practical constraints. In contrast to traditional sensing and rendezvous schemes, the joint sensing and rendezvous technique suggested in this research uses a partial number of channels for both sensing and rendezvous attempts. The challenge of finding vacant channels among the risk of occupancy is addressed by using the statistical distribution of unoccupied channels and rendezvous in terms of probability mass function and cumulative mass function.  With the right statistical distribution, the proposed method shows a lower rendezvous time for a guaranteed rendezvous. Our simulation results show that the joint sensing and rendezvous model outperforms traditional distributed rendezvous strategies in terms of rendezvous time in the large-scale and dynamic CRNs.

Cognitive radio, multi-radio rendezvous, joint sensing and rendezvous, prime number theory, rendezvous sta- tistical distribution, rendezvous probability