A Deep Learning-Based Approach for Radiation Pattern Synthesis of an Array Antenna
In this article, we propose a deep neural network (DNN)- for the radiation pattern synthesis of an antenna. The DNN utilizes the radiation patterns as inputs and the amplitude and phase of the antenna elements as outputs. Consequently, the radiation patterns of the array antenna can be easily obtain...
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| Veröffentlicht in: | IEEE access 2020, Vol.8, p.226059-226063 |
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| creator | Kim, Jae Hee Choi, Sang Won |
| description | In this article, we propose a deep neural network (DNN)- for the radiation pattern synthesis of an antenna. The DNN utilizes the radiation patterns as inputs and the amplitude and phase of the antenna elements as outputs. Consequently, the radiation patterns of the array antenna can be easily obtained from the outputs of the trained DNN, which are amplitude and phase of the antenna elements. However, it is difficult to determine the amplitude and phase of each antenna element from the desired pattern in an environment where inter-element coupling exists. For this purpose, 6,859 radiation pattern samples for a 4 \times 1 array patch antenna were generated by changing the phases of the antenna elements, and those patterns were leveraged to train the proposed DNN with low complexity. The radiation patterns of the ideal square and triangular array shapes, which are practically infeasible to implement, were used as inputs to the DNN. It was confirmed that the radiation pattern generated from the output signals of the DNN was very similar to the input radiation pattern. |
| doi_str_mv | 10.1109/ACCESS.2020.3045464 |
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The DNN utilizes the radiation patterns as inputs and the amplitude and phase of the antenna elements as outputs. Consequently, the radiation patterns of the array antenna can be easily obtained from the outputs of the trained DNN, which are amplitude and phase of the antenna elements. However, it is difficult to determine the amplitude and phase of each antenna element from the desired pattern in an environment where inter-element coupling exists. For this purpose, 6,859 radiation pattern samples for a <inline-formula> <tex-math notation="LaTeX">4 \times 1 </tex-math></inline-formula> array patch antenna were generated by changing the phases of the antenna elements, and those patterns were leveraged to train the proposed DNN with low complexity. The radiation patterns of the ideal square and triangular array shapes, which are practically infeasible to implement, were used as inputs to the DNN. It was confirmed that the radiation pattern generated from the output signals of the DNN was very similar to the input radiation pattern.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2020.3045464</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>PISCATAWAY: IEEE</publisher><subject>Amplitudes ; Antenna ; Antenna arrays ; Antenna radiation patterns ; Arrays ; Artificial neural networks ; Computer Science ; Computer Science, Information Systems ; Couplings ; Deep learning ; Engineering ; Engineering, Electrical & Electronic ; neural network ; Patch antennas ; Phased arrays ; radiation patterns ; Science & Technology ; Synthesis ; Technology ; Telecommunications ; Training</subject><ispartof>IEEE access, 2020, Vol.8, p.226059-226063</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>47</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000603716700001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c408t-69c20d44d1bcc0f88def13b9dc67af7dc5aa3169e8be4da3c6eff3ef74d07f013</citedby><cites>FETCH-LOGICAL-c408t-69c20d44d1bcc0f88def13b9dc67af7dc5aa3169e8be4da3c6eff3ef74d07f013</cites><orcidid>0000-0003-2193-928X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9296770$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>315,781,785,865,2103,2115,4025,27638,27928,27929,27930,28253,54938</link.rule.ids></links><search><creatorcontrib>Kim, Jae Hee</creatorcontrib><creatorcontrib>Choi, Sang Won</creatorcontrib><title>A Deep Learning-Based Approach for Radiation Pattern Synthesis of an Array Antenna</title><title>IEEE access</title><addtitle>Access</addtitle><addtitle>IEEE ACCESS</addtitle><description>In this article, we propose a deep neural network (DNN)- for the radiation pattern synthesis of an antenna. The DNN utilizes the radiation patterns as inputs and the amplitude and phase of the antenna elements as outputs. Consequently, the radiation patterns of the array antenna can be easily obtained from the outputs of the trained DNN, which are amplitude and phase of the antenna elements. However, it is difficult to determine the amplitude and phase of each antenna element from the desired pattern in an environment where inter-element coupling exists. For this purpose, 6,859 radiation pattern samples for a <inline-formula> <tex-math notation="LaTeX">4 \times 1 </tex-math></inline-formula> array patch antenna were generated by changing the phases of the antenna elements, and those patterns were leveraged to train the proposed DNN with low complexity. The radiation patterns of the ideal square and triangular array shapes, which are practically infeasible to implement, were used as inputs to the DNN. It was confirmed that the radiation pattern generated from the output signals of the DNN was very similar to the input radiation pattern.</description><subject>Amplitudes</subject><subject>Antenna</subject><subject>Antenna arrays</subject><subject>Antenna radiation patterns</subject><subject>Arrays</subject><subject>Artificial neural networks</subject><subject>Computer Science</subject><subject>Computer Science, Information Systems</subject><subject>Couplings</subject><subject>Deep learning</subject><subject>Engineering</subject><subject>Engineering, Electrical & Electronic</subject><subject>neural network</subject><subject>Patch antennas</subject><subject>Phased arrays</subject><subject>radiation patterns</subject><subject>Science & Technology</subject><subject>Synthesis</subject><subject>Technology</subject><subject>Telecommunications</subject><subject>Training</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>AOWDO</sourceid><sourceid>DOA</sourceid><recordid>eNqNkU9vGyEQxVdVKzVK8glyQcqxWpd_huW43aZpJEut4vaMMAwJVgIuYFX-9sHZKOmxXECj997M8Ou6C4IXhGD1eZymq_V6QTHFC4b5kgv-rjuhRKieLZl4_8_7Y3deyha3M7TSUp50tyP6CrBDKzA5hnjXfzEFHBp3u5yMvUc-ZXRrXDA1pIh-mlohR7Q-xHoPJRSUPDIRjTmbAxpjhRjNWffBm4cC5y_3aff729Wv6Xu_-nF9M42r3nI81F4oS7Hj3JGNtdgPgwNP2EY5K6Tx0tmlMaxNCcMGuDPMCvCegZfcYekxYafdzZzrktnqXQ6PJh90MkE_F1K-0ybXYB9AK8IItkIJhTGndhiosdJS33IHKtkx63LOamv_2UOpepv2ObbxNeWSUaGUpE3FZpXNqZQM_rUrwfrIQs8s9JGFfmHRXMPs-gub5IsNEC28OhsLgZkkQh6xkCnU56-e0j7WZv30_9amvpjVAeBNpagSUmL2BEBzpMk</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Kim, Jae Hee</creator><creator>Choi, Sang Won</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AOWDO</scope><scope>BLEPL</scope><scope>DTL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-2193-928X</orcidid></search><sort><creationdate>2020</creationdate><title>A Deep Learning-Based Approach for Radiation Pattern Synthesis of an Array Antenna</title><author>Kim, Jae Hee ; Choi, Sang Won</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-69c20d44d1bcc0f88def13b9dc67af7dc5aa3169e8be4da3c6eff3ef74d07f013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Amplitudes</topic><topic>Antenna</topic><topic>Antenna arrays</topic><topic>Antenna radiation patterns</topic><topic>Arrays</topic><topic>Artificial neural networks</topic><topic>Computer Science</topic><topic>Computer Science, Information Systems</topic><topic>Couplings</topic><topic>Deep learning</topic><topic>Engineering</topic><topic>Engineering, Electrical & Electronic</topic><topic>neural network</topic><topic>Patch antennas</topic><topic>Phased arrays</topic><topic>radiation patterns</topic><topic>Science & Technology</topic><topic>Synthesis</topic><topic>Technology</topic><topic>Telecommunications</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Jae Hee</creatorcontrib><creatorcontrib>Choi, Sang Won</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Jae Hee</au><au>Choi, Sang Won</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Deep Learning-Based Approach for Radiation Pattern Synthesis of an Array Antenna</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><stitle>IEEE ACCESS</stitle><date>2020</date><risdate>2020</risdate><volume>8</volume><spage>226059</spage><epage>226063</epage><pages>226059-226063</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>In this article, we propose a deep neural network (DNN)- for the radiation pattern synthesis of an antenna. The DNN utilizes the radiation patterns as inputs and the amplitude and phase of the antenna elements as outputs. Consequently, the radiation patterns of the array antenna can be easily obtained from the outputs of the trained DNN, which are amplitude and phase of the antenna elements. However, it is difficult to determine the amplitude and phase of each antenna element from the desired pattern in an environment where inter-element coupling exists. For this purpose, 6,859 radiation pattern samples for a <inline-formula> <tex-math notation="LaTeX">4 \times 1 </tex-math></inline-formula> array patch antenna were generated by changing the phases of the antenna elements, and those patterns were leveraged to train the proposed DNN with low complexity. The radiation patterns of the ideal square and triangular array shapes, which are practically infeasible to implement, were used as inputs to the DNN. It was confirmed that the radiation pattern generated from the output signals of the DNN was very similar to the input radiation pattern.</abstract><cop>PISCATAWAY</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2020.3045464</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0003-2193-928X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Amplitudes Antenna Antenna arrays Antenna radiation patterns Arrays Artificial neural networks Computer Science Computer Science, Information Systems Couplings Deep learning Engineering Engineering, Electrical & Electronic neural network Patch antennas Phased arrays radiation patterns Science & Technology Synthesis Technology Telecommunications Training |
| title | A Deep Learning-Based Approach for Radiation Pattern Synthesis of an Array Antenna |
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