Probe Selection and Power Weighting in Multiprobe OTA Testing: A Neural Network-Based Approach
Over-the-air (OTA) radiated testing is an efficient solution to evaluate the performance of multiple-input multiple-output (MIMO) capable devices, which can emulate realistic multipath channel conditions in a controlled manner within lab environment. In a multiprobe anechoic chamber- (MPAC-) based O...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2019/1392129 |
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| author | Yong Li Hao Sun Xingyu Chen Lijian Xin Xiang Zhang |
| author_facet | Yong Li Hao Sun Xingyu Chen Lijian Xin Xiang Zhang |
| author_sort | Yong Li |
| collection | DOAJ |
| description | Over-the-air (OTA) radiated testing is an efficient solution to evaluate the performance of multiple-input multiple-output (MIMO) capable devices, which can emulate realistic multipath channel conditions in a controlled manner within lab environment. In a multiprobe anechoic chamber- (MPAC-) based OTA setup, determining the most appropriate probe locations and their power weights is critical to improve the accuracy of channel emulation at reasonable system costs. In this paper, a novel approach based on neural networks (NNs) is proposed to derive suitable angular locations as well as power weights of OTA probe antennas; in particular, by using the regularization technique, active probe locations and their weights can be optimized simultaneously with only one training process of the proposed NN. Simulations demonstrate that compared with the convex optimization-based approach to perform probe selection in the literature, e.g., the well-known multishot algorithm, the proposed NN-based approach can yield similar channel emulation accuracy with significantly reduced computational complexity. |
| format | Article |
| id | doaj-art-fd077df96fec4d84a6e8c132211aa74d |
| institution | Kabale University |
| issn | 1687-5869 1687-5877 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-fd077df96fec4d84a6e8c132211aa74d2025-02-03T01:23:45ZengWileyInternational Journal of Antennas and Propagation1687-58691687-58772019-01-01201910.1155/2019/13921291392129Probe Selection and Power Weighting in Multiprobe OTA Testing: A Neural Network-Based ApproachYong Li0Hao Sun1Xingyu Chen2Lijian Xin3Xiang Zhang4Key Laboratory of Universal Wireless Communications (Ministry of Education), Beijing University of Posts and Telecommunications, Beijing 100876, ChinaKey Laboratory of Universal Wireless Communications (Ministry of Education), Beijing University of Posts and Telecommunications, Beijing 100876, ChinaDepartment of Electronic and Information Engineering, College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, ChinaKey Laboratory of Universal Wireless Communications (Ministry of Education), Beijing University of Posts and Telecommunications, Beijing 100876, ChinaChina Academy of Information and Communications Technology, Beijing 100191, ChinaOver-the-air (OTA) radiated testing is an efficient solution to evaluate the performance of multiple-input multiple-output (MIMO) capable devices, which can emulate realistic multipath channel conditions in a controlled manner within lab environment. In a multiprobe anechoic chamber- (MPAC-) based OTA setup, determining the most appropriate probe locations and their power weights is critical to improve the accuracy of channel emulation at reasonable system costs. In this paper, a novel approach based on neural networks (NNs) is proposed to derive suitable angular locations as well as power weights of OTA probe antennas; in particular, by using the regularization technique, active probe locations and their weights can be optimized simultaneously with only one training process of the proposed NN. Simulations demonstrate that compared with the convex optimization-based approach to perform probe selection in the literature, e.g., the well-known multishot algorithm, the proposed NN-based approach can yield similar channel emulation accuracy with significantly reduced computational complexity.http://dx.doi.org/10.1155/2019/1392129 |
| spellingShingle | Yong Li Hao Sun Xingyu Chen Lijian Xin Xiang Zhang Probe Selection and Power Weighting in Multiprobe OTA Testing: A Neural Network-Based Approach International Journal of Antennas and Propagation |
| title | Probe Selection and Power Weighting in Multiprobe OTA Testing: A Neural Network-Based Approach |
| title_full | Probe Selection and Power Weighting in Multiprobe OTA Testing: A Neural Network-Based Approach |
| title_fullStr | Probe Selection and Power Weighting in Multiprobe OTA Testing: A Neural Network-Based Approach |
| title_full_unstemmed | Probe Selection and Power Weighting in Multiprobe OTA Testing: A Neural Network-Based Approach |
| title_short | Probe Selection and Power Weighting in Multiprobe OTA Testing: A Neural Network-Based Approach |
| title_sort | probe selection and power weighting in multiprobe ota testing a neural network based approach |
| url | http://dx.doi.org/10.1155/2019/1392129 |
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