Implementation of Machine Learning and Artificial Intelligence Methods in Enhancing the Efficiency of Models of Antennas in Complicated Conditions

Abstract

Machine learning (ML) and artificial intelligence (AI) have been very beneficial in applying on optimization of antenna respectively as to complex environment in the modern communication systems. Many previous approaches to designing antennas may work well 
but cannot cope with the continuously increasing expectations in terms of performance and flexibility especially when it comes to communication in urban environment or within crowded networks and novel 5G/6G use cases. The application of AI and ML algorithms in the designing of antennas means that designers can easily improve other key parameters such as the radiation patterns, the frequency responses and the materials to be used. Such algorithms shall be capable of assessing the array pattern performance under various conditions including interference, environment and bandwidth demand for an automatic corrective action. Methods such as reinforcement learning and genetic algorithms aid in designing reconfigurable smart and smart antennas; they change with time. In addition to that, using optimization techniques rooted in AI, iterations of designs and evaluations are cut by merging the processes of simulation and evaluation checks. I have used integration of AI in a way to improve not only the efficiency and the performance of the antennae but it facilitates the development of new and sophisticated designs for complex segments such 
as IoT, satellite communication and auto mobile communications. The integration of AI with antenna technology therefore sets a new chapter on for development of sophisticated adaptive efficient and high performing antennas for emerging systems.