An Efficient Hybrid Analysis to Improve Data Rate Signal Transmission in Cognitive Radio Networks Using Multi- Hop

Abstract

Spectrum scarcity problems can be resolved with the emerging communiqué technologies known as cognitive radio (CR). Cognitive radio networks (CRNs) will give mobile users greater bandwidth via wirelessly heterogeneity design and dynamic spectrum acquisition methods. The Cognitive Radio Mobile Ad-Hoc Network (CR-MANET) idea of Adaptive Routing a new network paradigm may be realized by using the functions of spectrum management to overcome such difficulties. Secondary users (SUs) have the freedom to opportunistically explore and make use of the open spaces on licensed channels. When a primary user (PU) interferes with a licensed channel, this forces the SU to leave it and switch to an open channel. Because of the constant channel switching those results, SUs degrades as a result. In this result recommends a number of channels, number of hop CRN that uses a fuzzy decision-making system that is genetically optimized for channel selection, channel switching, and spectrum allocation. According to study, the suggested architecture achieves higher PDR, throughput, latency, and transmission time than fuzzy and genetic algorithms. Through simulations the result demonstrates significant improvements in data rate performance, making it a promising solution for enhancing communication efficiency in cognitive radio networks.

Proficient Bayesian Classifier for Predicting Congestion and Active Node Sensing Classification in Wireless Cognitive Radio

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