Multi-Objective Optimization of UAV Relay Deployment for Air-to-Ground Communications via Distributed Collaborative Beamforming
Uncrewed aerial vehicle (UAV) relay systems based on distributed collaborative beamforming (DCB) present significant opportunities for enhancing uplink transmission in emerging 6G wireless networks. However, the distributed nature of UAV positioning often results in beamforming degradation, leading...
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/11104851/ |
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| author | Yang Yang Xin Feng Jing Zhang Hongwei Yang Tingting Zheng |
| author_facet | Yang Yang Xin Feng Jing Zhang Hongwei Yang Tingting Zheng |
| author_sort | Yang Yang |
| collection | DOAJ |
| description | Uncrewed aerial vehicle (UAV) relay systems based on distributed collaborative beamforming (DCB) present significant opportunities for enhancing uplink transmission in emerging 6G wireless networks. However, the distributed nature of UAV positioning often results in beamforming degradation, leading to reduced communication quality. To address this issue, this paper proposes a flexible UAV relay deployment strategy under varying network constraints. The strategy operates in two stages: it first determines the minimum number of UAVs required to guarantee received signal strength (RSS) above a predefined threshold, and then jointly optimizes the UAV positions and excitation current weights by formulating a multi-objective optimization problem (MOP). The MOP aims to minimize the maximum sidelobe level (SLL) and propulsion energy consumption while maximizing the transmission rate. We propose an enhanced multi-objective evolutionary algorithm, BSINSGA-II, which integrates beetle swarm optimization (BSO) into the non-dominated sorting genetic algorithm II (NSGA-II). This integration enhances the global search capability and mitigates the risk of convergence to local optima. Furthermore, the good point set combined with K-means clustering is employed to generate a uniformly distributed initial population, thereby improving convergence performance and solution quality. Simulation results demonstrate that the proposed strategy not only determines the minimum number of UAVs for reliable relay communication, but also significantly reduces energy consumption, suppresses the maximum SLL and improves transmission rate compared to benchmark approaches. |
| format | Article |
| id | doaj-art-e753a3f3c7fb4512bb08b0de21ce2da3 |
| institution | Kabale University |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-e753a3f3c7fb4512bb08b0de21ce2da32025-08-25T23:19:03ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-0166437645010.1109/OJCOMS.2025.359413911104851Multi-Objective Optimization of UAV Relay Deployment for Air-to-Ground Communications via Distributed Collaborative BeamformingYang Yang0https://orcid.org/0009-0005-7019-2305Xin Feng1https://orcid.org/0000-0003-3187-9225Jing Zhang2https://orcid.org/0000-0002-3192-0358Hongwei Yang3https://orcid.org/0000-0002-6803-9185Tingting Zheng4https://orcid.org/0000-0002-3139-7798College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, ChinaCollege of Computer Science and Technology, Changchun University of Science and Technology, Changchun, ChinaCollege of Computer Science and Technology, Changchun University of Science and Technology, Changchun, ChinaCollege of Computer Science and Technology, Changchun University of Science and Technology, Changchun, ChinaCollege of Computer Science and Technology, Changchun University of Science and Technology, Changchun, ChinaUncrewed aerial vehicle (UAV) relay systems based on distributed collaborative beamforming (DCB) present significant opportunities for enhancing uplink transmission in emerging 6G wireless networks. However, the distributed nature of UAV positioning often results in beamforming degradation, leading to reduced communication quality. To address this issue, this paper proposes a flexible UAV relay deployment strategy under varying network constraints. The strategy operates in two stages: it first determines the minimum number of UAVs required to guarantee received signal strength (RSS) above a predefined threshold, and then jointly optimizes the UAV positions and excitation current weights by formulating a multi-objective optimization problem (MOP). The MOP aims to minimize the maximum sidelobe level (SLL) and propulsion energy consumption while maximizing the transmission rate. We propose an enhanced multi-objective evolutionary algorithm, BSINSGA-II, which integrates beetle swarm optimization (BSO) into the non-dominated sorting genetic algorithm II (NSGA-II). This integration enhances the global search capability and mitigates the risk of convergence to local optima. Furthermore, the good point set combined with K-means clustering is employed to generate a uniformly distributed initial population, thereby improving convergence performance and solution quality. Simulation results demonstrate that the proposed strategy not only determines the minimum number of UAVs for reliable relay communication, but also significantly reduces energy consumption, suppresses the maximum SLL and improves transmission rate compared to benchmark approaches.https://ieeexplore.ieee.org/document/11104851/UAV relay communicationflexible deploymentdistributed collaborative beamformingmulti-objective optimization |
| spellingShingle | Yang Yang Xin Feng Jing Zhang Hongwei Yang Tingting Zheng Multi-Objective Optimization of UAV Relay Deployment for Air-to-Ground Communications via Distributed Collaborative Beamforming IEEE Open Journal of the Communications Society UAV relay communication flexible deployment distributed collaborative beamforming multi-objective optimization |
| title | Multi-Objective Optimization of UAV Relay Deployment for Air-to-Ground Communications via Distributed Collaborative Beamforming |
| title_full | Multi-Objective Optimization of UAV Relay Deployment for Air-to-Ground Communications via Distributed Collaborative Beamforming |
| title_fullStr | Multi-Objective Optimization of UAV Relay Deployment for Air-to-Ground Communications via Distributed Collaborative Beamforming |
| title_full_unstemmed | Multi-Objective Optimization of UAV Relay Deployment for Air-to-Ground Communications via Distributed Collaborative Beamforming |
| title_short | Multi-Objective Optimization of UAV Relay Deployment for Air-to-Ground Communications via Distributed Collaborative Beamforming |
| title_sort | multi objective optimization of uav relay deployment for air to ground communications via distributed collaborative beamforming |
| topic | UAV relay communication flexible deployment distributed collaborative beamforming multi-objective optimization |
| url | https://ieeexplore.ieee.org/document/11104851/ |
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