International Journal of Computing and Mathematics

The field of wireless communication is currently facing several important obstacles, attracting the thought of many researchers. Cognitive radio is characterized as a multidimensional, independent radio system that learns to basis, plan, and determines future performance to attain user requirements from its experiences. Intelligent management, allowance, and use of scarce resources are needed by such an extremely diverse radio situation. Issues such as sensing and allocation of spectrum, environmental learning i.e., adaptability and learning ability attract the attention of many learning and optimization strategies for soft computing, such as neural networks, fuzzy logic, genetic algorithm and swarm intelligence. Cognitive radio is one of the most influential new technologies promising to deal with such circumstances. Based on software-defined radio technology, cognitive radio systems use intelligent software packages that augment their transceivers with the greatly striking properties of self-awareness, flexibility, and learning ability. To monitor and adjust the radio device from the physical layer to the apex of the communication stack, the cognitive engine behind the radio incorporates sensing, learning, switching, and optimization algorithms. 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.