NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
With the proliferation of smart devices and the emergence of high-bandwidth applications, Unmanned Aerial Vehicle (UAV)-assisted Device-to-Device (D2D) communications and Non-Orthogonal Multiple Access (NOMA) technologies are increasingly becoming important means of coping with the scarcity of the s...
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MDPI AG
2025-01-01
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| author | Guowei Wu Guifen Chen Xinglong Gu |
| author_facet | Guowei Wu Guifen Chen Xinglong Gu |
| author_sort | Guowei Wu |
| collection | DOAJ |
| description | With the proliferation of smart devices and the emergence of high-bandwidth applications, Unmanned Aerial Vehicle (UAV)-assisted Device-to-Device (D2D) communications and Non-Orthogonal Multiple Access (NOMA) technologies are increasingly becoming important means of coping with the scarcity of the spectrum and with high data demand in future wireless networks. However, the efficient coordination of these techniques in complex and changing 3D environments still faces many challenges. To this end, this paper proposes a NOMA-based multi-UAV-assisted D2D communication model in which multiple UAVs are deployed in 3D space to act as airborne base stations to serve ground-based cellular users with D2D clusters. In order to maximize the system throughput, this study constructs an optimization problem of joint channel assignment, trajectory design, and power control, and on the basis of these points, this study proposes a joint dynamic hypergraph Multi-Agent Deep Q Network (DH-MDQN) algorithm. The dynamic hypergraph method is first used to construct dynamic simple edges and hyperedges and to transform them into directed graphs for efficient dynamic coloring to optimize the channel allocation process; subsequently, in terms of trajectory design and power control, the problem is modeled as a multi-agent Markov Decision Process (MDP), and the Multi-Agent Deep Q Network (MDQN) algorithm is used to collaboratively determine the trajectory design and power control of the UAVs. Simulation results show the following: (1) the proposed algorithm can achieve higher system throughput than several other benchmark algorithms with different numbers of D2D clusters, different D2D cluster communication spacing, and different UAV sizes; (2) the proposed algorithm designs UAV trajectory optimization with a 27% improvement in system throughput compared to the 2D trajectory; and (3) in the NOMA scenario, compared to the case of no decoding order constraints, the system throughput shows on average a 34% improvement. |
| format | Article |
| id | doaj-art-cc7865d498dd4a0f9c5de2169a4c08d0 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Drones |
| spelling | doaj-art-cc7865d498dd4a0f9c5de2169a4c08d02025-01-24T13:29:50ZengMDPI AGDrones2504-446X2025-01-01916210.3390/drones9010062NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication NetworksGuowei Wu0Guifen Chen1Xinglong Gu2College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaCollege of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaCollege of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaWith the proliferation of smart devices and the emergence of high-bandwidth applications, Unmanned Aerial Vehicle (UAV)-assisted Device-to-Device (D2D) communications and Non-Orthogonal Multiple Access (NOMA) technologies are increasingly becoming important means of coping with the scarcity of the spectrum and with high data demand in future wireless networks. However, the efficient coordination of these techniques in complex and changing 3D environments still faces many challenges. To this end, this paper proposes a NOMA-based multi-UAV-assisted D2D communication model in which multiple UAVs are deployed in 3D space to act as airborne base stations to serve ground-based cellular users with D2D clusters. In order to maximize the system throughput, this study constructs an optimization problem of joint channel assignment, trajectory design, and power control, and on the basis of these points, this study proposes a joint dynamic hypergraph Multi-Agent Deep Q Network (DH-MDQN) algorithm. The dynamic hypergraph method is first used to construct dynamic simple edges and hyperedges and to transform them into directed graphs for efficient dynamic coloring to optimize the channel allocation process; subsequently, in terms of trajectory design and power control, the problem is modeled as a multi-agent Markov Decision Process (MDP), and the Multi-Agent Deep Q Network (MDQN) algorithm is used to collaboratively determine the trajectory design and power control of the UAVs. Simulation results show the following: (1) the proposed algorithm can achieve higher system throughput than several other benchmark algorithms with different numbers of D2D clusters, different D2D cluster communication spacing, and different UAV sizes; (2) the proposed algorithm designs UAV trajectory optimization with a 27% improvement in system throughput compared to the 2D trajectory; and (3) in the NOMA scenario, compared to the case of no decoding order constraints, the system throughput shows on average a 34% improvement.https://www.mdpi.com/2504-446X/9/1/62unmanned aerial vehiclesnon-orthogonal multiple accessD2Ddynamic hypergraphmulti-agent reinforcement learning |
| spellingShingle | Guowei Wu Guifen Chen Xinglong Gu NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks Drones unmanned aerial vehicles non-orthogonal multiple access D2D dynamic hypergraph multi-agent reinforcement learning |
| title | NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks |
| title_full | NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks |
| title_fullStr | NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks |
| title_full_unstemmed | NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks |
| title_short | NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks |
| title_sort | noma based rate optimization for multi uav assisted d2d communication networks |
| topic | unmanned aerial vehicles non-orthogonal multiple access D2D dynamic hypergraph multi-agent reinforcement learning |
| url | https://www.mdpi.com/2504-446X/9/1/62 |
| work_keys_str_mv | AT guoweiwu nomabasedrateoptimizationformultiuavassistedd2dcommunicationnetworks AT guifenchen nomabasedrateoptimizationformultiuavassistedd2dcommunicationnetworks AT xinglonggu nomabasedrateoptimizationformultiuavassistedd2dcommunicationnetworks |