Deep Reinforcemnet Learning for Robust Beamforming in Integrated Sensing, Communication and Power Transmission Systems
A communication network integrating multiple modes can effectively support the sustainable development of next-generation wireless communications. Integrated sensing, communication, and power transfer (ISCPT) represents an emerging technological paradigm that not only facilitates information transmi...
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MDPI AG
2025-01-01
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| author | Chenfei Xie Yue Xiu Songjie Yang Qilong Miao Lu Chen Yong Gao Zhongpei Zhang |
| author_facet | Chenfei Xie Yue Xiu Songjie Yang Qilong Miao Lu Chen Yong Gao Zhongpei Zhang |
| author_sort | Chenfei Xie |
| collection | DOAJ |
| description | A communication network integrating multiple modes can effectively support the sustainable development of next-generation wireless communications. Integrated sensing, communication, and power transfer (ISCPT) represents an emerging technological paradigm that not only facilitates information transmission but also enables environmental sensing and wireless power transfer. To achieve optimal beamforming in transmission, it is crucial to satisfy multiple constraints, including quality of service (QoS), radar sensing accuracy, and power transfer efficiency, while ensuring fundamental system performance. The presence of multiple parametric constraints makes the problem a non-convex optimization challenge, underscoring the need for a solution that balances low computational complexity with high precision. Additionally, the accuracy of channel state information (CSI) is pivotal in determining the achievable rate, as imperfect or incomplete CSI can significantly degrade system performance and beamforming efficiency. Deep reinforcement learning (DRL), a machine learning technique where an agent learns by interacting with its environment, offers a promising approach that can dynamically optimize system performance through adaptive decision-making strategies. In this paper, we propose a DRL-based ISCPT framework, which effectively manages complex environmental states and continuously adjusts variables related to sensing, communication, and energy harvesting to enhance overall system efficiency and reliability. The achievable rate upper bound can be inferred through robust, learnable beamforming in the ISCPT system. Our results demonstrate that DRL-based algorithms significantly improve resource allocation, power management, and information transmission, particularly in dynamic and uncertain environments with imperfect CSI. |
| format | Article |
| id | doaj-art-ee6bae561359490f8266e2791e78f632 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ee6bae561359490f8266e2791e78f6322025-01-24T13:48:44ZengMDPI AGSensors1424-82202025-01-0125238810.3390/s25020388Deep Reinforcemnet Learning for Robust Beamforming in Integrated Sensing, Communication and Power Transmission SystemsChenfei Xie0Yue Xiu1Songjie Yang2Qilong Miao3Lu Chen4Yong Gao5Zhongpei Zhang6National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, ChinaNational Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, ChinaNational Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, ChinaNational Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, ChinaA communication network integrating multiple modes can effectively support the sustainable development of next-generation wireless communications. Integrated sensing, communication, and power transfer (ISCPT) represents an emerging technological paradigm that not only facilitates information transmission but also enables environmental sensing and wireless power transfer. To achieve optimal beamforming in transmission, it is crucial to satisfy multiple constraints, including quality of service (QoS), radar sensing accuracy, and power transfer efficiency, while ensuring fundamental system performance. The presence of multiple parametric constraints makes the problem a non-convex optimization challenge, underscoring the need for a solution that balances low computational complexity with high precision. Additionally, the accuracy of channel state information (CSI) is pivotal in determining the achievable rate, as imperfect or incomplete CSI can significantly degrade system performance and beamforming efficiency. Deep reinforcement learning (DRL), a machine learning technique where an agent learns by interacting with its environment, offers a promising approach that can dynamically optimize system performance through adaptive decision-making strategies. In this paper, we propose a DRL-based ISCPT framework, which effectively manages complex environmental states and continuously adjusts variables related to sensing, communication, and energy harvesting to enhance overall system efficiency and reliability. The achievable rate upper bound can be inferred through robust, learnable beamforming in the ISCPT system. Our results demonstrate that DRL-based algorithms significantly improve resource allocation, power management, and information transmission, particularly in dynamic and uncertain environments with imperfect CSI.https://www.mdpi.com/1424-8220/25/2/388integrating sensingcommunicationpower transferdeep reinforcement learningrobust beamformingimperfect channel state information |
| spellingShingle | Chenfei Xie Yue Xiu Songjie Yang Qilong Miao Lu Chen Yong Gao Zhongpei Zhang Deep Reinforcemnet Learning for Robust Beamforming in Integrated Sensing, Communication and Power Transmission Systems Sensors integrating sensing communication power transfer deep reinforcement learning robust beamforming imperfect channel state information |
| title | Deep Reinforcemnet Learning for Robust Beamforming in Integrated Sensing, Communication and Power Transmission Systems |
| title_full | Deep Reinforcemnet Learning for Robust Beamforming in Integrated Sensing, Communication and Power Transmission Systems |
| title_fullStr | Deep Reinforcemnet Learning for Robust Beamforming in Integrated Sensing, Communication and Power Transmission Systems |
| title_full_unstemmed | Deep Reinforcemnet Learning for Robust Beamforming in Integrated Sensing, Communication and Power Transmission Systems |
| title_short | Deep Reinforcemnet Learning for Robust Beamforming in Integrated Sensing, Communication and Power Transmission Systems |
| title_sort | deep reinforcemnet learning for robust beamforming in integrated sensing communication and power transmission systems |
| topic | integrating sensing communication power transfer deep reinforcement learning robust beamforming imperfect channel state information |
| url | https://www.mdpi.com/1424-8220/25/2/388 |
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