Simulated Annealing Assisted Sparse Array Selection Utilizing Deep Learning
This paper proposes simulated annealing (SA) assisted deep learning (DL) based sparse array selection approach. Conventional DL-based antenna selectors are primarily data-driven techniques. As a result, the required dataset is generated by listing all possible combinations of selecting <inline-fo...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9623448/ |
| _version_ | 1818684216157667328 |
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| author | Steven Wandale Koichi Ichige |
| author_facet | Steven Wandale Koichi Ichige |
| author_sort | Steven Wandale |
| collection | DOAJ |
| description | This paper proposes simulated annealing (SA) assisted deep learning (DL) based sparse array selection approach. Conventional DL-based antenna selectors are primarily data-driven techniques. As a result, the required dataset is generated by listing all possible combinations of selecting <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> sensors given <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> uniform array, which is computationally expensive. A simulated annealing algorithm is proposed to assist dataset generation as an initializer to circumvent the above limitation. The SA algorithm sequentially samples and optimizes the subarrays that constitute the training data samples while retaining specific array characteristics. Hence, it simplifies the dataset annotation as most array configurations generated contain desired properties, thereby reducing the computation complexity of the overall data annotation processes. Therefore, the initializer reduces computation costs related to data generation considerably. Simulation examples show that using the dataset generated by the proposed method improves the DL-based array selector’s accuracy compared to the one generated by the conventional random sampler. Moreover, the realized sparse arrays show better sparse array configuration characteristics and enhanced DOA estimation performance. |
| first_indexed | 2024-12-17T10:47:06Z |
| format | Article |
| id | doaj.art-6641cfa0fa3f4c9eb3403a62097c2617 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T10:47:06Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6641cfa0fa3f4c9eb3403a62097c26172022-12-21T21:52:05ZengIEEEIEEE Access2169-35362021-01-01915690715691410.1109/ACCESS.2021.31298569623448Simulated Annealing Assisted Sparse Array Selection Utilizing Deep LearningSteven Wandale0https://orcid.org/0000-0002-4118-5000Koichi Ichige1https://orcid.org/0000-0001-7663-6919Department of Electrical and Computer Engineering, Graduate School of Engineering Sciences, Yokohama National University, Hodogaya-ku, JapanDepartment of Electrical and Computer Engineering, Graduate School of Engineering Sciences, Yokohama National University, Hodogaya-ku, JapanThis paper proposes simulated annealing (SA) assisted deep learning (DL) based sparse array selection approach. Conventional DL-based antenna selectors are primarily data-driven techniques. As a result, the required dataset is generated by listing all possible combinations of selecting <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> sensors given <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> uniform array, which is computationally expensive. A simulated annealing algorithm is proposed to assist dataset generation as an initializer to circumvent the above limitation. The SA algorithm sequentially samples and optimizes the subarrays that constitute the training data samples while retaining specific array characteristics. Hence, it simplifies the dataset annotation as most array configurations generated contain desired properties, thereby reducing the computation complexity of the overall data annotation processes. Therefore, the initializer reduces computation costs related to data generation considerably. Simulation examples show that using the dataset generated by the proposed method improves the DL-based array selector’s accuracy compared to the one generated by the conventional random sampler. Moreover, the realized sparse arrays show better sparse array configuration characteristics and enhanced DOA estimation performance.https://ieeexplore.ieee.org/document/9623448/Antenna selectiondirection-of-arrival estimationdeep learningsimulated annealingsparse arrays |
| spellingShingle | Steven Wandale Koichi Ichige Simulated Annealing Assisted Sparse Array Selection Utilizing Deep Learning IEEE Access Antenna selection direction-of-arrival estimation deep learning simulated annealing sparse arrays |
| title | Simulated Annealing Assisted Sparse Array Selection Utilizing Deep Learning |
| title_full | Simulated Annealing Assisted Sparse Array Selection Utilizing Deep Learning |
| title_fullStr | Simulated Annealing Assisted Sparse Array Selection Utilizing Deep Learning |
| title_full_unstemmed | Simulated Annealing Assisted Sparse Array Selection Utilizing Deep Learning |
| title_short | Simulated Annealing Assisted Sparse Array Selection Utilizing Deep Learning |
| title_sort | simulated annealing assisted sparse array selection utilizing deep learning |
| topic | Antenna selection direction-of-arrival estimation deep learning simulated annealing sparse arrays |
| url | https://ieeexplore.ieee.org/document/9623448/ |
| work_keys_str_mv | AT stevenwandale simulatedannealingassistedsparsearrayselectionutilizingdeeplearning AT koichiichige simulatedannealingassistedsparsearrayselectionutilizingdeeplearning |