Resnet Associated Cross-Layered Routing in Cognitive Radio Network

In this paper, we discuss a cross-layer routing protocol that is derived from machine learning and a CRN. The protocol is described in detail. This protocol was developed with the intention of enhancing the routing performance of a reconfigurable network and maximising the effective use of available bandwidth during data transfer. A distributed controller is given instructions by the system to carry out activities such as load balancing, data collection on the immediate region, and the generation of the routes that are the most efficient. This is done so that the system can achieve its intended purpose. In order to specify clustering, the CRN uses a machine-learning-assisted routing protocol. This protocol enables controllers to work together with primary or secondary users on the forwarding plane. To demonstrate that the reconfigurable CRN is more scalable and robust in terms of the amount of residual energy and resources it requires, tests are carried out using standard CRN routing models. These tests are carried out to prove that the reconfigurable CRN.