Computational Intelligence in Communication Networks
Kiran Hemanthraj Muloor, Somesh Kumar Sahu, Tapan Kumar Behera, Debabrata Samanta · 2024
In Computational Intelligence, data processing and machine learning have become driving forces, which are widely used for intelligence application development and hardware products with AI capabilities. In this paper, we describe the architecture of Computational Intelligence (CI), describing its core technologies, including neurocomputing, granular computing, fuzzy sets, evolutionary algorithms, and the design methodology that underlies the technology. Communication is currently being improved by the use of Artificial Intelligence (AI) techniques to improve the efficiency of communication. For the development of successful communication network models, Computational Intelligence (CI) systems possess characteristics such as adaptability, fault tolerance, high computational speed, and error resilience in the face of noisy input. This chapter will discuss neural networks, fuzzy systems, evolutionary computation, artificial immune systems, swarm intelligence, and soft computing. Each field of research is synthesized and compared to provide a clear understanding of existing challenges and identify promising new directions. We conclude that communication network systems can be designed and analyzed using CI. The results of this study provide a better understanding of AI techniques for enhancing communication networks and shed light on future research directions.