Machine-Learning-Based Framework for Coding Digital Receiving Array with Few RF Channels
A novel framework for a low-cost coding digital receiving array based on machine learning (ML-CDRA) is proposed in this paper. The received full-array signals are encoded into a few radio frequency (RF) channels, and decoded by an artificial neural network in real-time. The encoding and decoding net...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/20/5086 |
| _version_ | 1797470055470989312 |
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| author | Lei Xiao Yubing Han Zuxin Weng |
| author_facet | Lei Xiao Yubing Han Zuxin Weng |
| author_sort | Lei Xiao |
| collection | DOAJ |
| description | A novel framework for a low-cost coding digital receiving array based on machine learning (ML-CDRA) is proposed in this paper. The received full-array signals are encoded into a few radio frequency (RF) channels, and decoded by an artificial neural network in real-time. The encoding and decoding networks are studied in detail, including the implementation of the encoding network, the loss function and the complexity of the decoding network. A generalized form of loss function is presented by constraint with maximum likelihood, signal sparsity, and noise. Moreover, a feasible loss function is given as an example and the derivations for back propagation are successively derived. In addition, a real-time processing implementation architecture for ML-CDRA is presented based on the commercial chips. It is possible to implement by adding an additional FPGA on the hardware basis of full-channel DRA. ML-CDRA requires fewer RF channels than the traditional full-channel array, while maintaining a similar digital beamforming (DBF) performance. This provides a practical solution to the typical problems in the existing low-cost DBF systems, such as synchronization, moving target compensation, and being disabled at a low signal-to-noise ratio. The performance of ML-CDRA is evaluated in simulations. |
| first_indexed | 2024-03-09T19:31:23Z |
| format | Article |
| id | doaj.art-182a501eb37c4414b641a785eeda10ba |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T19:31:23Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-182a501eb37c4414b641a785eeda10ba2023-11-24T02:18:55ZengMDPI AGRemote Sensing2072-42922022-10-011420508610.3390/rs14205086Machine-Learning-Based Framework for Coding Digital Receiving Array with Few RF ChannelsLei Xiao0Yubing Han1Zuxin Weng2School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaA novel framework for a low-cost coding digital receiving array based on machine learning (ML-CDRA) is proposed in this paper. The received full-array signals are encoded into a few radio frequency (RF) channels, and decoded by an artificial neural network in real-time. The encoding and decoding networks are studied in detail, including the implementation of the encoding network, the loss function and the complexity of the decoding network. A generalized form of loss function is presented by constraint with maximum likelihood, signal sparsity, and noise. Moreover, a feasible loss function is given as an example and the derivations for back propagation are successively derived. In addition, a real-time processing implementation architecture for ML-CDRA is presented based on the commercial chips. It is possible to implement by adding an additional FPGA on the hardware basis of full-channel DRA. ML-CDRA requires fewer RF channels than the traditional full-channel array, while maintaining a similar digital beamforming (DBF) performance. This provides a practical solution to the typical problems in the existing low-cost DBF systems, such as synchronization, moving target compensation, and being disabled at a low signal-to-noise ratio. The performance of ML-CDRA is evaluated in simulations.https://www.mdpi.com/2072-4292/14/20/5086coding digital receiving arraymachine learninglow-cost DBF systemfew-RF-channelencoding and decoding |
| spellingShingle | Lei Xiao Yubing Han Zuxin Weng Machine-Learning-Based Framework for Coding Digital Receiving Array with Few RF Channels Remote Sensing coding digital receiving array machine learning low-cost DBF system few-RF-channel encoding and decoding |
| title | Machine-Learning-Based Framework for Coding Digital Receiving Array with Few RF Channels |
| title_full | Machine-Learning-Based Framework for Coding Digital Receiving Array with Few RF Channels |
| title_fullStr | Machine-Learning-Based Framework for Coding Digital Receiving Array with Few RF Channels |
| title_full_unstemmed | Machine-Learning-Based Framework for Coding Digital Receiving Array with Few RF Channels |
| title_short | Machine-Learning-Based Framework for Coding Digital Receiving Array with Few RF Channels |
| title_sort | machine learning based framework for coding digital receiving array with few rf channels |
| topic | coding digital receiving array machine learning low-cost DBF system few-RF-channel encoding and decoding |
| url | https://www.mdpi.com/2072-4292/14/20/5086 |
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