کنفرانس مهندسی برق ایران

صفحه اصلی / بیست و نهمین کنفرانس مهندسی برق ایران

Optimized 5G-MMW Compact Yagi-Uda Antenna Based on Machine Learning Methodology

نویسندگان :

Alireza Jafarieh¹ Mahdi Nouri² Hamid Behroozi³

1- Sharif university of technology 2- Sharif university of technology 3- Sharif university of technology

کلمات کلیدی :

Machine learning (ML), 5G communication, MMW, Yagi-Uda antenna, microstrip antenna, surrogate-based optimization (SBO).

چکیده :

The fifth generation (5G) of the mobile communication should provide a faster latency rate, wider Bandwidth (BW), and higher Gain (G) in comparison with older systems, (e.g. fourth generation (4G)). For 5G applications, the millimeter wave (MMW) antennas seem to be a suitable choice due to their small size. Owing to a large number of design parameters, designing an optimum antenna that can satisfy the 5G conditions is a very challenging task. In the meanwhile, using machine learning (ML) approaches to find the optimum design is an appropriate solution. Surrogate-based optimization (SBO) can handle the high computational cost of ML approaches, especially when the number of design parameters is large. The microstrip Yagi-Uda antennas play an important role in 5G communication systems due to their high BW and directive G.

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بیشتر

ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 42.3.2