Unmanned Aerial Vehicle with Underlaid Device-to-Device Communications: Performance and Tradeoffs
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Abstract (arXiv)
In this paper, the deployment of an unmanned aerial vehicle (UAV) as a flying base station used to provide on the fly wireless communications to a given geographical area is analyzed. In particular, the co-existence between the UAV, that is transmitting data in the downlink, and an underlaid device-todevice (D2D) communication network is considered. For this model, a tractable analytical framework for the coverage and rate analysis is derived. Two scenarios are considered: a static UAV and a mobile UAV. In the first scenario, the average coverage probability and the system sum-rate for the users in the area are derived as a function of the UAV altitude and the number of D2D users. In the second scenario, using the disk covering problem, the minimum number of stop points that the UAV needs to visit in order to completely cover the area is computed. Furthermore, considering multiple retransmissions for the UAV and D2D users, the overall outage probability of the D2D users is derived. Simulation and analytical results show that, depending on the density of D2D users, optimal values for the UAV altitude exist for which the system sum-rate and the coverage probability are maximized. Moreover, our results also show that, by enabling the UAV to intelligently move over the target area, the total required transmit power of UAV while covering the entire area, is minimized. Finally, in order to provide a full coverage for the area of interest, the tradeoff between the coverage and delay, in terms of the number of stop points, is discussed.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2015.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 3.
6 papers.
- 20213D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learningcited by 8
- 2021Hybrid Policy Learning for Energy-Latency Tradeoff in MEC-Assisted VR Video Servicecited by 5
- 2021UAV-Assisted Communication in Remote Disaster Areas using Imitation Learningcited by 3
- 2023Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learningcited by 2
- 2023Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networkscited by 1
- 2023Energy-Efficient Cellular-Connected UAV Swarm Control Optimizationcited by 1
- 2023Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning15 Jun 2023
- 2023Energy-Efficient Cellular-Connected UAV Swarm Control Optimization18 Mar 2023
- 2023Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks15 Mar 2023
- 20213D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning6 Jun 2021
- 2021UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning2 Apr 2021
- 2021Hybrid Policy Learning for Energy-Latency Tradeoff in MEC-Assisted VR Video Service2 Apr 2021