Effects of metamaterials on MIMO antennas for X-band radar applications and parameter optimization with a machine learning model: A review
Radars are at the core of numerous real-world applications in healthcare monitoring and autonomous driving due to the rapid expansion of the communication system. MIMO (Multiple-Input Multiple-Output) antennas are an essential component of radar systems. The effect of mutual coupling degraded the pe...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2023-04-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0142886 |
| _version_ | 1797771825750474752 |
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| author | Jyothsna Undrakonda Ratna Kumari Upadhyayula |
| author_facet | Jyothsna Undrakonda Ratna Kumari Upadhyayula |
| author_sort | Jyothsna Undrakonda |
| collection | DOAJ |
| description | Radars are at the core of numerous real-world applications in healthcare monitoring and autonomous driving due to the rapid expansion of the communication system. MIMO (Multiple-Input Multiple-Output) antennas are an essential component of radar systems. The effect of mutual coupling degraded the performance of these antennas. This article comprehensively reviewed the metamaterial-based decoupling technique for antenna design and provided a comparison with other decoupling techniques. The occurrence and variety of current information, the sophistication of processing, and the low cost of data storage all contribute to the increased interest in using machine learning to find optimal solutions in a variety of fields. This article introduces and investigates machine learning applications in antenna design. This paper discusses implementing different machine learning models to optimize primary antenna performance, reduce mutual coupling, and increase the bandwidth. Various numerical results from synthetically generated and experimental datasets and about two specific applications are presented as a conclusion. These allow readers to evaluate the effectiveness of particular methods and compare them in terms of precision and computational effort. |
| first_indexed | 2024-03-12T21:43:10Z |
| format | Article |
| id | doaj.art-389f0da902964ceeb58c4a829331a5a2 |
| institution | Directory Open Access Journal |
| issn | 2158-3226 |
| language | English |
| last_indexed | 2024-03-12T21:43:10Z |
| publishDate | 2023-04-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj.art-389f0da902964ceeb58c4a829331a5a22023-07-26T14:57:19ZengAIP Publishing LLCAIP Advances2158-32262023-04-01134040701040701-1910.1063/5.0142886Effects of metamaterials on MIMO antennas for X-band radar applications and parameter optimization with a machine learning model: A reviewJyothsna Undrakonda0Ratna Kumari Upadhyayula1Department of EECE, GITAM Deemed to be University, Visakhapatnam, Andhra Pradesh, IndiaDepartment of EECE, GITAM Deemed to be University, Visakhapatnam, Andhra Pradesh, IndiaRadars are at the core of numerous real-world applications in healthcare monitoring and autonomous driving due to the rapid expansion of the communication system. MIMO (Multiple-Input Multiple-Output) antennas are an essential component of radar systems. The effect of mutual coupling degraded the performance of these antennas. This article comprehensively reviewed the metamaterial-based decoupling technique for antenna design and provided a comparison with other decoupling techniques. The occurrence and variety of current information, the sophistication of processing, and the low cost of data storage all contribute to the increased interest in using machine learning to find optimal solutions in a variety of fields. This article introduces and investigates machine learning applications in antenna design. This paper discusses implementing different machine learning models to optimize primary antenna performance, reduce mutual coupling, and increase the bandwidth. Various numerical results from synthetically generated and experimental datasets and about two specific applications are presented as a conclusion. These allow readers to evaluate the effectiveness of particular methods and compare them in terms of precision and computational effort.http://dx.doi.org/10.1063/5.0142886 |
| spellingShingle | Jyothsna Undrakonda Ratna Kumari Upadhyayula Effects of metamaterials on MIMO antennas for X-band radar applications and parameter optimization with a machine learning model: A review AIP Advances |
| title | Effects of metamaterials on MIMO antennas for X-band radar applications and parameter optimization with a machine learning model: A review |
| title_full | Effects of metamaterials on MIMO antennas for X-band radar applications and parameter optimization with a machine learning model: A review |
| title_fullStr | Effects of metamaterials on MIMO antennas for X-band radar applications and parameter optimization with a machine learning model: A review |
| title_full_unstemmed | Effects of metamaterials on MIMO antennas for X-band radar applications and parameter optimization with a machine learning model: A review |
| title_short | Effects of metamaterials on MIMO antennas for X-band radar applications and parameter optimization with a machine learning model: A review |
| title_sort | effects of metamaterials on mimo antennas for x band radar applications and parameter optimization with a machine learning model a review |
| url | http://dx.doi.org/10.1063/5.0142886 |
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