Design of 5G Antenna and it\'s optimization using AI (Machine learning)

Novel broadband substrate integrated waveguide cavity-backed slot antenna for 5G millimeter-wave wireless applications is proposed. This new antenna topology with constrained footprint achieves great bandwidth enhancement compared with previously proposed designs, by introducing multi-mode op

Project Title

Design of 5G Antenna and it\'s optimization using AI (Machine learning)

Project Area of Specialization

Artificial Intelligence

Project Summary

Novel broadband substrate integrated waveguide

cavity-backed slot antenna for 5G millimeter-wave wireless applications is proposed. This new antenna topology with constrained footprint achieves great bandwidth enhancement compared with previously proposed designs, by introducing multi-mode operation using loaded cross-shaped slot and unbalanced metallic vias. Moreover, a new machine-learning-assisted optimization method with additional feature is proposed to optimize antenna parameters. The above mentioned design-optimization procedure yields an antenna with measured fractional impedance bandwidth of 23.7%. The measured maximum gain of the antenna is 7.4 dBi within the entire bandwidth.

Project Objectives

Nowadays, substrate integrated waveguide (SIW) like cavity-backed slot (CBS) antennas have been widely inves-tigated for various applications such as satellite communications and wireless communications, due to its outstanding advantages of low profile, easy integration with planar circuits, excellent loss performance and good directional radiation performance. Among different design requirements, bandwidth enhancement stands as an important topic especially for mmWave broadband applications. In shorting vias were introduced in the SIW cavity to realize triple and quad-resonance SIW CBS antennas, and achieved bandwidths of 15.2% and 17.5%, respectively. In, a coupled half-mode/quarter-mode SIW CBS antenna was proposed and a measured fractional impedance bandwidth of 11.7% was achieved.

Project Implementation Method

Given the operating principle of the quad-mode SIW CBS

antenna, there exist strong mutual coupling and propagation effects between the design parameters. The population-based metaheuristic optimization method is utilized here associated with the machine-learning method. By manipulating with the training set construction and the fitness function, the MLO-MAF is proposed to optimize the parameters of the antenna

at the designated frequency band.

Benefits of the Project

A novel broadband SIW CBS antenna has been proposed for 5G mmWave applications. Using the cross-shaped slot and the unbalanced via placement, a new topology has been designed to achieve quad-mode excitation of the SIW cavity. Furthermore, a new optimization algorithm has been introduced to optimize the antenna dimensions. Measurement results show that the prototype can achieve impedance bandwidth of 23.7%. The proposed SIW CBS antenna provides excellent features including broad bandwidth, low profile, and fabrication ease.

Technical Details of Final Deliverable

By using the above proposed antenna we can achieve more bandwidth and the main objective is to optimize the antenna by using AI. That is done in this project. With this optimization, antenna can manage it's own parameters by itself. Which means that it will be an intelligent antenna with the help of AI.

And our final deliverables are antenna Design and AI optimization of antenna.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Telecommunication

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com