Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) System
Due to the limited ability of a single unmanned aerial vehicle (UAV), group unmanned aerial vehicles (UAVs) have attracted more attention in communication and radar fields. The use of an integrated sensing and communication (ISAC) system can make communication and radar modules share a radar module’...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/3/441 |
| _version_ | 1797488324564221952 |
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| author | Min Wang Peng Chen Zhenxin Cao Yun Chen |
| author_facet | Min Wang Peng Chen Zhenxin Cao Yun Chen |
| author_sort | Min Wang |
| collection | DOAJ |
| description | Due to the limited ability of a single unmanned aerial vehicle (UAV), group unmanned aerial vehicles (UAVs) have attracted more attention in communication and radar fields. The use of an integrated sensing and communication (ISAC) system can make communication and radar modules share a radar module’s resources, coupled with efficient resource allocation methods. It can effectively solve the problem of inadequate UAV resources and the low utilization rate of resources. In this paper, the resource allocation problem is addressed for group UAVs to achieve a trade-off between the detection and communication performance, where the ISAC system is equipped in group UAVs. The resource allocation problem is described by an optimization problem, but with group UAVs, the problem is complex and cannot be solved efficiently. Compared with the traditional resource allocation scheme, which needs a lot of calculation or sample set problems, a novel reinforcement-learning-based method is proposed. We formulate a new reward function by combining mutual information (MI) and the communication rate (CR). The MI describes the radar detection performance, and the CR is for wireless communication. Simulation results show that compared with the traditional Kuhn Munkres (KM) or the deep neural network (DNN) methods, this method has better performance with the increase in problem complexity. Additionally, the execution time of this scheme is close to that of the DNN scheme, and it is better than the KM algorithm. |
| first_indexed | 2024-03-10T00:00:30Z |
| format | Article |
| id | doaj.art-8bd695df414947faa63d231eaf7ff210 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T00:00:30Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-8bd695df414947faa63d231eaf7ff2102023-11-23T16:16:59ZengMDPI AGElectronics2079-92922022-02-0111344110.3390/electronics11030441Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) SystemMin Wang0Peng Chen1Zhenxin Cao2Yun Chen3State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, ChinaState Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, ChinaState Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, ChinaState Key Laboratory of ASIC and System, Fudan University, Shanghai 200433, ChinaDue to the limited ability of a single unmanned aerial vehicle (UAV), group unmanned aerial vehicles (UAVs) have attracted more attention in communication and radar fields. The use of an integrated sensing and communication (ISAC) system can make communication and radar modules share a radar module’s resources, coupled with efficient resource allocation methods. It can effectively solve the problem of inadequate UAV resources and the low utilization rate of resources. In this paper, the resource allocation problem is addressed for group UAVs to achieve a trade-off between the detection and communication performance, where the ISAC system is equipped in group UAVs. The resource allocation problem is described by an optimization problem, but with group UAVs, the problem is complex and cannot be solved efficiently. Compared with the traditional resource allocation scheme, which needs a lot of calculation or sample set problems, a novel reinforcement-learning-based method is proposed. We formulate a new reward function by combining mutual information (MI) and the communication rate (CR). The MI describes the radar detection performance, and the CR is for wireless communication. Simulation results show that compared with the traditional Kuhn Munkres (KM) or the deep neural network (DNN) methods, this method has better performance with the increase in problem complexity. Additionally, the execution time of this scheme is close to that of the DNN scheme, and it is better than the KM algorithm.https://www.mdpi.com/2079-9292/11/3/441group UAVsresources allocationreinforcement learningintegrated sensing and communication (ISAC) system |
| spellingShingle | Min Wang Peng Chen Zhenxin Cao Yun Chen Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) System Electronics group UAVs resources allocation reinforcement learning integrated sensing and communication (ISAC) system |
| title | Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) System |
| title_full | Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) System |
| title_fullStr | Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) System |
| title_full_unstemmed | Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) System |
| title_short | Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) System |
| title_sort | reinforcement learning based uavs resource allocation for integrated sensing and communication isac system |
| topic | group UAVs resources allocation reinforcement learning integrated sensing and communication (ISAC) system |
| url | https://www.mdpi.com/2079-9292/11/3/441 |
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