Research on Soft Computing Techniques for Cognitive Radio Networks

Abstract

Numerous significant challenges are currently plaguing the field of wireless communication, drawing the attention of numerous scholars. Cognitive radio is defined as an autonomous, multifaceted radio technology that learns from its experiences to base, plan, and predict potential outcomes to meet user needs. Intelligent management, allowance, and use of scarce resources are needed by such an extremely diverse radio situation. Numerous learning and optimization techniques for soft computing, including brain networks, informal logic, evolutionary algorithms, and cluster intelligence, are drawn to problems like spectrum detection and allocation, environmental learning, or adaptability and learning ability. One of the most significant new technologies that promises to address such situations is neural radio. Cognitive radio systems, which are based on software-defined radio technology, employ clever software packages that enhance their transmitters with the remarkably remarkable capabilities of consciousness of oneself, adaptability, and learning. The intelligence engine that powers the radio uses detecting, instruction, shifting, and algorithmic optimization. to monitor and modify the radio device from the material layer to the top of the transmission stack. This paper provides a critical analysis of various approaches to soft computing applied to cognitive radio problems and also points out different avenues for the study about it.