DRL-Based UAV Path Planning for Coverage Hole Avoidance: Energy Consumption and Outage Time Minimization Trade-Offs
<p dir="ltr">Coverage holes pose critical challenges to reliability of wireless networks and their quality of service (QoS) and therefore should be avoided in the coverage design. In this paper, we address this issue through the deployment of unmanned aerial vehicles (UAVs) as mobile...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513523995901952 |
|---|---|
| author | Bahareh Jafari (22501715) |
| author2 | Mazen Hasna (16904661) Hossein Pishro-Nik (22501718) Nizar Zorba (16888728) Tamer Khattab (16870086) Hamid Saeedi (21399992) |
| author2_role | author author author author author |
| author_facet | Bahareh Jafari (22501715) Mazen Hasna (16904661) Hossein Pishro-Nik (22501718) Nizar Zorba (16888728) Tamer Khattab (16870086) Hamid Saeedi (21399992) |
| author_role | author |
| dc.creator.none.fl_str_mv | Bahareh Jafari (22501715) Mazen Hasna (16904661) Hossein Pishro-Nik (22501718) Nizar Zorba (16888728) Tamer Khattab (16870086) Hamid Saeedi (21399992) |
| dc.date.none.fl_str_mv | 2025-04-28T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/ojcoms.2025.3564837 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/DRL-Based_UAV_Path_Planning_for_Coverage_Hole_Avoidance_Energy_Consumption_and_Outage_Time_Minimization_Trade-Offs/30845393 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Communications engineering Control engineering, mechatronics and robotics Information and computing sciences Distributed computing and systems software Machine learning Coverage hole QoS Path planning UAV Mechanical energy Deep reinforcement learning (DRL) |
| dc.title.none.fl_str_mv | DRL-Based UAV Path Planning for Coverage Hole Avoidance: Energy Consumption and Outage Time Minimization Trade-Offs |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Coverage holes pose critical challenges to reliability of wireless networks and their quality of service (QoS) and therefore should be avoided in the coverage design. In this paper, we address this issue through the deployment of unmanned aerial vehicles (UAVs) as mobile base stations, and we propose specific UAV path planning. A point is said to be in a coverage hole if the coverage probability for that point is below a certain threshold, e.g., 90%. This definition is more suitable for applications such as surveillance or sensor networks. In this paper, we target applications such as wireless communications for which QoS requirement allow only for short time disconnections, i.e., minimal outage time. As such, in addition to avoiding coverage holes, we should also make the outage time as small as possible. By deploying a deep reinforcement learning algorithm, we find optimal UAV paths based on the two families of trajectories: spiral and oval curves, to tackle different design considerations and constraints, in terms of QoS, energy consumption and coverage hole avoidance. We show that for a typical point on the cell, there is a trade-off between minimizing the maximum outage time length and consumed mechanical energy. Our observations indicate that such a trade-off is more pronounced for spiral trajectories compared to oval trajectories, but both of them are useful depending on the QoS and energy constraints imposed by the system.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Communications Society<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojcoms.2025.3564837" target="_blank">https://dx.doi.org/10.1109/ojcoms.2025.3564837</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_d0539ccfaabfe7670fef19dc3bd06230 |
| identifier_str_mv | 10.1109/ojcoms.2025.3564837 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30845393 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | DRL-Based UAV Path Planning for Coverage Hole Avoidance: Energy Consumption and Outage Time Minimization Trade-OffsBahareh Jafari (22501715)Mazen Hasna (16904661)Hossein Pishro-Nik (22501718)Nizar Zorba (16888728)Tamer Khattab (16870086)Hamid Saeedi (21399992)EngineeringCommunications engineeringControl engineering, mechatronics and roboticsInformation and computing sciencesDistributed computing and systems softwareMachine learningCoverage holeQoSPath planningUAVMechanical energyDeep reinforcement learning (DRL)<p dir="ltr">Coverage holes pose critical challenges to reliability of wireless networks and their quality of service (QoS) and therefore should be avoided in the coverage design. In this paper, we address this issue through the deployment of unmanned aerial vehicles (UAVs) as mobile base stations, and we propose specific UAV path planning. A point is said to be in a coverage hole if the coverage probability for that point is below a certain threshold, e.g., 90%. This definition is more suitable for applications such as surveillance or sensor networks. In this paper, we target applications such as wireless communications for which QoS requirement allow only for short time disconnections, i.e., minimal outage time. As such, in addition to avoiding coverage holes, we should also make the outage time as small as possible. By deploying a deep reinforcement learning algorithm, we find optimal UAV paths based on the two families of trajectories: spiral and oval curves, to tackle different design considerations and constraints, in terms of QoS, energy consumption and coverage hole avoidance. We show that for a typical point on the cell, there is a trade-off between minimizing the maximum outage time length and consumed mechanical energy. Our observations indicate that such a trade-off is more pronounced for spiral trajectories compared to oval trajectories, but both of them are useful depending on the QoS and energy constraints imposed by the system.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Communications Society<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojcoms.2025.3564837" target="_blank">https://dx.doi.org/10.1109/ojcoms.2025.3564837</a></p>2025-04-28T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/ojcoms.2025.3564837https://figshare.com/articles/journal_contribution/DRL-Based_UAV_Path_Planning_for_Coverage_Hole_Avoidance_Energy_Consumption_and_Outage_Time_Minimization_Trade-Offs/30845393CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/308453932025-04-28T03:00:00Z |
| spellingShingle | DRL-Based UAV Path Planning for Coverage Hole Avoidance: Energy Consumption and Outage Time Minimization Trade-Offs Bahareh Jafari (22501715) Engineering Communications engineering Control engineering, mechatronics and robotics Information and computing sciences Distributed computing and systems software Machine learning Coverage hole QoS Path planning UAV Mechanical energy Deep reinforcement learning (DRL) |
| status_str | publishedVersion |
| title | DRL-Based UAV Path Planning for Coverage Hole Avoidance: Energy Consumption and Outage Time Minimization Trade-Offs |
| title_full | DRL-Based UAV Path Planning for Coverage Hole Avoidance: Energy Consumption and Outage Time Minimization Trade-Offs |
| title_fullStr | DRL-Based UAV Path Planning for Coverage Hole Avoidance: Energy Consumption and Outage Time Minimization Trade-Offs |
| title_full_unstemmed | DRL-Based UAV Path Planning for Coverage Hole Avoidance: Energy Consumption and Outage Time Minimization Trade-Offs |
| title_short | DRL-Based UAV Path Planning for Coverage Hole Avoidance: Energy Consumption and Outage Time Minimization Trade-Offs |
| title_sort | DRL-Based UAV Path Planning for Coverage Hole Avoidance: Energy Consumption and Outage Time Minimization Trade-Offs |
| topic | Engineering Communications engineering Control engineering, mechatronics and robotics Information and computing sciences Distributed computing and systems software Machine learning Coverage hole QoS Path planning UAV Mechanical energy Deep reinforcement learning (DRL) |