Cognitive Radio Networks: An AI Enabled Approach

As the need for data transfer in wireless networks grows, cognitive radios are likely to meet a large part of that need. Cognitive radio nodes can sense what's going on around them, figure out what's best for using the radio spectrum by allocating resources in terms of time, frequency, and space, and then act on that information. AI and machine learning are often combined with cognitive radio to create smart and flexible sharing, which makes real-time operations more effective. This research talks about cognitive radio networks and their goals, problems, resources, and limits. There is a research into how learning affects cognitive radios and methods for AI and machine learning are shown. This section gives an outline of the newest research on how machine learning can be used in cognitive radios. A number of AI techniques are used to organize the literature review, such as neural networks, fuzzy logic, case-based reasoning, entropy, Bayesian analysis, Markov models, multi-agent systems, and artificial bee colony algorithms. Cognitive radio uses and possible learning barriers are also talked about.