Metamaterial based MIMO Antenna and its parameter Optimization Machine Learning Model

What is it about?

Multiple-input multiple-output (MIMO) antennas represent a highly advanced component of radar communication systems. Through the utilization of radar technology, it becomes possible to make estimations regarding the range, position, and velocity of a given object. In order to obtain the aforementioned information, it is imperative that the antenna possess high isolation, gain, and optimal resolution towards the object of interest. This can be accomplished through the utilization of metamaterials in the design process. The distinctive characteristics of these properties alter the electromagnetic reaction of the antenna in specific frequency applications. In order to achieve precise and unambiguous results, it is necessary to optimize the parameters of the antenna. To address this issue, simple and easy algorithms have been introduced. The utilization of machine learning models is considered a highly effective approach in optimizing antenna parameters. This method enables the establishment of a correlation between variable parameters and the ability to forecast future outcomes without the need for inputs.

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Why is it important?

Numerous researchers have previously proposed various techniques for achieving effective isolation and amplification. There remains potential for improving the essential parameters of the antenna. Various optimization techniques, such as artificial neural networks and supervised models, have been developed to enhance the antenna parameters. In order to enhance the correlation between input and output parameters, it is imperative to develop novel optimization algorithms. Machine learning is a highly effective algorithmic approach for optimizing antenna parameters in an efficient manner.

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