Adaptive TCS creation and assignment mechanism for cognitive radio network using Golden Eagle optimized Hybrid Multilayer Perceptron-Convolutional Neural Network

Abstract

The increasing number of wireless devices tends to produce a scarcity of spectrum resources. Cognitive Radio is introduced to tackle this scarcity issue since the cognitive radio users are unlicensed users and utilize the underutilized licensed spectrum without impacting any damage to the licensed users. However, this CR generates a target channel sequence (TCS) to assign the channel for the multi-user while occurring handoff. Besides it also faces some challenges like the fair assignment of TCS problems to multiple secondary users, channel access conflicts, channel obsolesces effects during the creation of TCS. To tackle the issues, we proposed a novel Three-stage mechanism to create and assign TCS for the multi-users along with the hybrid optimized Multilayer perceptron-based Convolution neural network approach. Here, the protection to safeguard VoIP communication can be performed with the aid of a proposed hybrid MLP-CNN-GEO approach. The experimental analysis depicts that the proposed work reduces handoff delays and call-drops, and assigns a fair and dynamic channel for secondary users.