Low-Resolution Quantized Precoding for Multiple-Input Multiple-Output Dual-Functional Radar–Communication Systems Used for Target Sensing
Dual-functional radar–communication systems are extensively employed for the detection and control of unmanned aerial vehicle groups and play crucial roles in scenario monitoring. In this study, we address the downlink precoding problem in large-scale multi-user multiple-input multiple-output dual-f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/198 |
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| author | Xiang Feng Zhongqing Zhao Jiongshi Wang Jian Wang Zhanfeng Zhao Zhiquan Zhou |
| author_facet | Xiang Feng Zhongqing Zhao Jiongshi Wang Jian Wang Zhanfeng Zhao Zhiquan Zhou |
| author_sort | Xiang Feng |
| collection | DOAJ |
| description | Dual-functional radar–communication systems are extensively employed for the detection and control of unmanned aerial vehicle groups and play crucial roles in scenario monitoring. In this study, we address the downlink precoding problem in large-scale multi-user multiple-input multiple-output dual-function radar–communication systems equipped with low-resolution quantized digital-to-analog converters. To tackle this issue, we develop a weighted optimization framework that minimizes the mean squared error between the transmitted symbols and their estimates while satisfying specific radar performance requirements. Due to the complexity introduced by discrete constraints, we decompose the original problem into three sub-problems to reduce computational burden. Furthermore, we propose a dynamic projection refinement algorithm within the alternating direction method of multiplier framework to efficiently solve these sub-problems. Numerical experiments demonstrate that our proposed method outperforms existing state-of-the-art techniques, particularly in terms of bit error rate in low signal-to-noise ratio scenarios. |
| format | Article |
| id | doaj-art-c2b6d77145884f0b946a1cb049d94aca |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-c2b6d77145884f0b946a1cb049d94aca2025-01-24T13:47:42ZengMDPI AGRemote Sensing2072-42922025-01-0117219810.3390/rs17020198Low-Resolution Quantized Precoding for Multiple-Input Multiple-Output Dual-Functional Radar–Communication Systems Used for Target SensingXiang Feng0Zhongqing Zhao1Jiongshi Wang2Jian Wang3Zhanfeng Zhao4Zhiquan Zhou5School of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, ChinaSchool of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, ChinaSchool of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, ChinaBeijing Institute of Remote Sensing Equipment, Beijing 100854, ChinaSchool of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, ChinaSchool of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, ChinaDual-functional radar–communication systems are extensively employed for the detection and control of unmanned aerial vehicle groups and play crucial roles in scenario monitoring. In this study, we address the downlink precoding problem in large-scale multi-user multiple-input multiple-output dual-function radar–communication systems equipped with low-resolution quantized digital-to-analog converters. To tackle this issue, we develop a weighted optimization framework that minimizes the mean squared error between the transmitted symbols and their estimates while satisfying specific radar performance requirements. Due to the complexity introduced by discrete constraints, we decompose the original problem into three sub-problems to reduce computational burden. Furthermore, we propose a dynamic projection refinement algorithm within the alternating direction method of multiplier framework to efficiently solve these sub-problems. Numerical experiments demonstrate that our proposed method outperforms existing state-of-the-art techniques, particularly in terms of bit error rate in low signal-to-noise ratio scenarios.https://www.mdpi.com/2072-4292/17/2/198dual-functional radar–communicationlarge-scale multiple-input multiple-outputlow-resolution quantized precodingdiscrete optimization |
| spellingShingle | Xiang Feng Zhongqing Zhao Jiongshi Wang Jian Wang Zhanfeng Zhao Zhiquan Zhou Low-Resolution Quantized Precoding for Multiple-Input Multiple-Output Dual-Functional Radar–Communication Systems Used for Target Sensing Remote Sensing dual-functional radar–communication large-scale multiple-input multiple-output low-resolution quantized precoding discrete optimization |
| title | Low-Resolution Quantized Precoding for Multiple-Input Multiple-Output Dual-Functional Radar–Communication Systems Used for Target Sensing |
| title_full | Low-Resolution Quantized Precoding for Multiple-Input Multiple-Output Dual-Functional Radar–Communication Systems Used for Target Sensing |
| title_fullStr | Low-Resolution Quantized Precoding for Multiple-Input Multiple-Output Dual-Functional Radar–Communication Systems Used for Target Sensing |
| title_full_unstemmed | Low-Resolution Quantized Precoding for Multiple-Input Multiple-Output Dual-Functional Radar–Communication Systems Used for Target Sensing |
| title_short | Low-Resolution Quantized Precoding for Multiple-Input Multiple-Output Dual-Functional Radar–Communication Systems Used for Target Sensing |
| title_sort | low resolution quantized precoding for multiple input multiple output dual functional radar communication systems used for target sensing |
| topic | dual-functional radar–communication large-scale multiple-input multiple-output low-resolution quantized precoding discrete optimization |
| url | https://www.mdpi.com/2072-4292/17/2/198 |
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