Beamforming and resource optimization in UAV integrated sensing and communication network with edge computing
To address the dependence of traditional integrated sensing and communication network mode on ground infrastructure, the unmanned aerial vehicle (UAV) with edge computing server and radar transceiver was proposed to solve the problems of high-power consumption, signal blocking, and coverage blind sp...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2023-09-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023172/ |
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| _version_ | 1841539167905906688 |
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| author | Bin LI Sicong PENG Zesong FEI |
| author_facet | Bin LI Sicong PENG Zesong FEI |
| author_sort | Bin LI |
| collection | DOAJ |
| description | To address the dependence of traditional integrated sensing and communication network mode on ground infrastructure, the unmanned aerial vehicle (UAV) with edge computing server and radar transceiver was proposed to solve the problems of high-power consumption, signal blocking, and coverage blind spots in complex scenarios.Firstly, under the conditions of satisfying the user’s transmission power, radar estimation information rate and task offloading proportion limit, the system energy consumption was minimized by jointly optimizing UAV radar beamforming, computing resource allocation, task offloading, user transmission power, and UAV flight trajectory.Secondly, the non-convex optimization problem was reformulated as a Markov decision process, and the proximal policy optimization method based deep reinforcement learning was used to achieve the optimal solution.Simulation results show that the proposed algorithm has a faster training speed and can reduce the system energy consumption effectively while satisfying the sensing and computing delay requirements. |
| format | Article |
| id | doaj-art-09086d2f4c944e61a3bf222f57c92cab |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-09086d2f4c944e61a3bf222f57c92cab2025-01-14T07:23:37ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-09-014422823759836265Beamforming and resource optimization in UAV integrated sensing and communication network with edge computingBin LISicong PENGZesong FEITo address the dependence of traditional integrated sensing and communication network mode on ground infrastructure, the unmanned aerial vehicle (UAV) with edge computing server and radar transceiver was proposed to solve the problems of high-power consumption, signal blocking, and coverage blind spots in complex scenarios.Firstly, under the conditions of satisfying the user’s transmission power, radar estimation information rate and task offloading proportion limit, the system energy consumption was minimized by jointly optimizing UAV radar beamforming, computing resource allocation, task offloading, user transmission power, and UAV flight trajectory.Secondly, the non-convex optimization problem was reformulated as a Markov decision process, and the proximal policy optimization method based deep reinforcement learning was used to achieve the optimal solution.Simulation results show that the proposed algorithm has a faster training speed and can reduce the system energy consumption effectively while satisfying the sensing and computing delay requirements.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023172/integrated sensing-communication-computation networkUAVdeep reinforcement learningresource allocation and optimization |
| spellingShingle | Bin LI Sicong PENG Zesong FEI Beamforming and resource optimization in UAV integrated sensing and communication network with edge computing Tongxin xuebao integrated sensing-communication-computation network UAV deep reinforcement learning resource allocation and optimization |
| title | Beamforming and resource optimization in UAV integrated sensing and communication network with edge computing |
| title_full | Beamforming and resource optimization in UAV integrated sensing and communication network with edge computing |
| title_fullStr | Beamforming and resource optimization in UAV integrated sensing and communication network with edge computing |
| title_full_unstemmed | Beamforming and resource optimization in UAV integrated sensing and communication network with edge computing |
| title_short | Beamforming and resource optimization in UAV integrated sensing and communication network with edge computing |
| title_sort | beamforming and resource optimization in uav integrated sensing and communication network with edge computing |
| topic | integrated sensing-communication-computation network UAV deep reinforcement learning resource allocation and optimization |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023172/ |
| work_keys_str_mv | AT binli beamformingandresourceoptimizationinuavintegratedsensingandcommunicationnetworkwithedgecomputing AT sicongpeng beamformingandresourceoptimizationinuavintegratedsensingandcommunicationnetworkwithedgecomputing AT zesongfei beamformingandresourceoptimizationinuavintegratedsensingandcommunicationnetworkwithedgecomputing |