A Comprehensive Review on Conventional and Machine Learning-Assisted Design of 5G Microstrip Patch Antenna

OpenAlex · English

A significant advancement in wireless communication has occurred over the past couple of decades. Nowadays, people rely more on services offered by the Internet of Things, cloud computing, and big data analytics-based applications. Higher data rates, faster transmission/reception times, more coverage, and higher throughputs are all necessary for these emerging applications. 5G technology supports all these features. Antennas, one of the most crucial components of modern wireless gadgets, must be manufactured specifically to meet the market’s growing demand for fast and intelligent goods. This study reviews various 5G antenna types in detail, categorizing them into two categories: conventional design approaches and machine learning-assisted optimization approaches, followed by a comparative study on various 5G antennas reported in publications. Machine learning (ML) is receiving a lot of emphasis because of its ability to identify optimal outcomes in several areas, and it is expected to be a key component of our future technology. ML is demonstrating an evident future in antenna design optimization by predicting antenna behavior and expediting optimization with accuracy and efficiency. The analysis of performance metrics used to evaluate 5G antenna performance is another focus of the assessment. Open research problems are also investigated, allowing researchers to fill up current research gaps.