A Comprehensive Review on Conventional and Machine Learning-Assisted Design of 5G Microstrip Patch Antenna
<p dir="ltr">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/...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , |
| منشور في: |
2024
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513549559136256 |
|---|---|
| author | Nupur Chhaule (21224987) |
| author2 | Chaitali Koley (12964250) Sudip Mandal (1760785) Ahmet Onen (20838293) Taha Selim Ustun (16869915) |
| author2_role | author author author author |
| author_facet | Nupur Chhaule (21224987) Chaitali Koley (12964250) Sudip Mandal (1760785) Ahmet Onen (20838293) Taha Selim Ustun (16869915) |
| author_role | author |
| dc.creator.none.fl_str_mv | Nupur Chhaule (21224987) Chaitali Koley (12964250) Sudip Mandal (1760785) Ahmet Onen (20838293) Taha Selim Ustun (16869915) |
| dc.date.none.fl_str_mv | 2024-09-27T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/electronics13193819 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Comprehensive_Review_on_Conventional_and_Machine_Learning-Assisted_Design_of_5G_Microstrip_Patch_Antenna/28909931 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Communications engineering Information and computing sciences Artificial intelligence Machine learning Artificial neural network (ANN) modeling Defected ground structure (DGS) 5G Technology Genetic algorithm (GA) Machine Learning (ML) Microstrip patch antenna (MPA) Particle swarm optimization (PSO) |
| dc.title.none.fl_str_mv | A Comprehensive Review on Conventional and Machine Learning-Assisted Design of 5G Microstrip Patch Antenna |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Electronics<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/electronics13193819" target="_blank">https://dx.doi.org/10.3390/electronics13193819</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_6ee1bc33aa72b248af799d1433fc5caf |
| identifier_str_mv | 10.3390/electronics13193819 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28909931 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Comprehensive Review on Conventional and Machine Learning-Assisted Design of 5G Microstrip Patch AntennaNupur Chhaule (21224987)Chaitali Koley (12964250)Sudip Mandal (1760785)Ahmet Onen (20838293)Taha Selim Ustun (16869915)EngineeringCommunications engineeringInformation and computing sciencesArtificial intelligenceMachine learningArtificial neural network (ANN) modelingDefected ground structure (DGS)5G TechnologyGenetic algorithm (GA)Machine Learning (ML)Microstrip patch antenna (MPA)Particle swarm optimization (PSO)<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Electronics<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/electronics13193819" target="_blank">https://dx.doi.org/10.3390/electronics13193819</a></p>2024-09-27T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/electronics13193819https://figshare.com/articles/journal_contribution/A_Comprehensive_Review_on_Conventional_and_Machine_Learning-Assisted_Design_of_5G_Microstrip_Patch_Antenna/28909931CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/289099312024-09-27T03:00:00Z |
| spellingShingle | A Comprehensive Review on Conventional and Machine Learning-Assisted Design of 5G Microstrip Patch Antenna Nupur Chhaule (21224987) Engineering Communications engineering Information and computing sciences Artificial intelligence Machine learning Artificial neural network (ANN) modeling Defected ground structure (DGS) 5G Technology Genetic algorithm (GA) Machine Learning (ML) Microstrip patch antenna (MPA) Particle swarm optimization (PSO) |
| status_str | publishedVersion |
| title | A Comprehensive Review on Conventional and Machine Learning-Assisted Design of 5G Microstrip Patch Antenna |
| title_full | A Comprehensive Review on Conventional and Machine Learning-Assisted Design of 5G Microstrip Patch Antenna |
| title_fullStr | A Comprehensive Review on Conventional and Machine Learning-Assisted Design of 5G Microstrip Patch Antenna |
| title_full_unstemmed | A Comprehensive Review on Conventional and Machine Learning-Assisted Design of 5G Microstrip Patch Antenna |
| title_short | A Comprehensive Review on Conventional and Machine Learning-Assisted Design of 5G Microstrip Patch Antenna |
| title_sort | A Comprehensive Review on Conventional and Machine Learning-Assisted Design of 5G Microstrip Patch Antenna |
| topic | Engineering Communications engineering Information and computing sciences Artificial intelligence Machine learning Artificial neural network (ANN) modeling Defected ground structure (DGS) 5G Technology Genetic algorithm (GA) Machine Learning (ML) Microstrip patch antenna (MPA) Particle swarm optimization (PSO) |