Interference Management in UAV-Assisted Multi-Cell Networks
This article considers a multi-cell wireless network comprising of conventional user equipment (UE), sensor devices and unmanned aerial vehicles (UAVs) or drones. UAVs are used to assist a base station, e.g., improve coverage or collect data from sensor devices. The problem at hand is to optimize th...
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MDPI AG
2025-06-01
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| author | Muchen Jiang Honglin Ren Yongxing Qi Ting Wu |
| author_facet | Muchen Jiang Honglin Ren Yongxing Qi Ting Wu |
| author_sort | Muchen Jiang |
| collection | DOAJ |
| description | This article considers a multi-cell wireless network comprising of conventional user equipment (UE), sensor devices and unmanned aerial vehicles (UAVs) or drones. UAVs are used to assist a base station, e.g., improve coverage or collect data from sensor devices. The problem at hand is to optimize the (i) sub-carrier assigned to a cell or base station, (ii) position of each UAV, and (iii) transmit power of the following nodes: base stations and UAVs. We outline a two-stage approach to maximize the fairness-aware sum-rate of UE and UAVs. In the first stage, a genetic algorithm (GA)-based approach is used to assign a sub-band to all cells and to determine the location of each UAV. Then, in the second stage, a linear program is used to determine the transmit power of UE and UAVs. The results demonstrate that our proposed two-stage approach achieves approximately 97.43% of the optimal fairness-aware sum-rate obtained via brute-force search. It also attains on average 98.78% of the performance of a computationally intensive benchmark that requires over 478% longer run-time. Furthermore, it outperforms a conventional GA-based sub-band allocation heuristic by 221.39%. |
| format | Article |
| id | doaj-art-94c54c8c65ea490ea5e7ea333f114f1a |
| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Information |
| spelling | doaj-art-94c54c8c65ea490ea5e7ea333f114f1a2025-08-20T02:21:12ZengMDPI AGInformation2078-24892025-06-0116648110.3390/info16060481Interference Management in UAV-Assisted Multi-Cell NetworksMuchen Jiang0Honglin Ren1Yongxing Qi2Ting Wu3Hangzhou Innovation Institute, Beihang University, Hangzhou 310052, ChinaSchool of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, AustraliaHangzhou Innovation Institute, Beihang University, Hangzhou 310052, ChinaHangzhou Innovation Institute, Beihang University, Hangzhou 310052, ChinaThis article considers a multi-cell wireless network comprising of conventional user equipment (UE), sensor devices and unmanned aerial vehicles (UAVs) or drones. UAVs are used to assist a base station, e.g., improve coverage or collect data from sensor devices. The problem at hand is to optimize the (i) sub-carrier assigned to a cell or base station, (ii) position of each UAV, and (iii) transmit power of the following nodes: base stations and UAVs. We outline a two-stage approach to maximize the fairness-aware sum-rate of UE and UAVs. In the first stage, a genetic algorithm (GA)-based approach is used to assign a sub-band to all cells and to determine the location of each UAV. Then, in the second stage, a linear program is used to determine the transmit power of UE and UAVs. The results demonstrate that our proposed two-stage approach achieves approximately 97.43% of the optimal fairness-aware sum-rate obtained via brute-force search. It also attains on average 98.78% of the performance of a computationally intensive benchmark that requires over 478% longer run-time. Furthermore, it outperforms a conventional GA-based sub-band allocation heuristic by 221.39%.https://www.mdpi.com/2078-2489/16/6/481OFDMAresource allocationsub-bandstransmit powerinterference |
| spellingShingle | Muchen Jiang Honglin Ren Yongxing Qi Ting Wu Interference Management in UAV-Assisted Multi-Cell Networks Information OFDMA resource allocation sub-bands transmit power interference |
| title | Interference Management in UAV-Assisted Multi-Cell Networks |
| title_full | Interference Management in UAV-Assisted Multi-Cell Networks |
| title_fullStr | Interference Management in UAV-Assisted Multi-Cell Networks |
| title_full_unstemmed | Interference Management in UAV-Assisted Multi-Cell Networks |
| title_short | Interference Management in UAV-Assisted Multi-Cell Networks |
| title_sort | interference management in uav assisted multi cell networks |
| topic | OFDMA resource allocation sub-bands transmit power interference |
| url | https://www.mdpi.com/2078-2489/16/6/481 |
| work_keys_str_mv | AT muchenjiang interferencemanagementinuavassistedmulticellnetworks AT honglinren interferencemanagementinuavassistedmulticellnetworks AT yongxingqi interferencemanagementinuavassistedmulticellnetworks AT tingwu interferencemanagementinuavassistedmulticellnetworks |