Unmanned Aerial Vehicle With Underlaid Device-to-Device Communications: Performance and Tradeoffs

By Mohammad Mozaffari, Walid Saad, Medhi Bennis, Merouane Debbah. 2015

1,126 citations recorded by the source corpus.

In this paper, the deployment of an unmanned aerial vehicle (UAV) as a flying base station used to provide the fly wireless communications to a given geographical area is analyzed. In particular, the coexistence between the UAV, that is transmitting data in the downlink, and an underlaid device-to-device (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, the optimal values for the UAV altitude, which lead to the maximum system sum-rate and coverage probability, exist. 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, can be minimized. Finally, in order to provide full coverage for the area of interest, the tradeoff between the coverage and delay, in terms of the number of stop points, is discussed.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Electrical and Electronic Engineering

Citations in the corpus

Wireless Network Intelligence at the Edge (2018, neutral): “[17] M.”
Federated Learning for Ultra-Reliable Low-Latency V2V Communications (2018, background): “This impacts the worst case network queuing latency (as well as end-to-end latency [1], [6], [7]) and thus, needs to be properly addressed.”
Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications (2018, background): “are coupled, ensuring low queuing latency is required to achieve the much coveted target end-to-end latency of 1ms. This, in turn, necessitates efﬁcient radio resource management (RRM) techniques [5]–[7]. Furthermore, the increased energy consumption and its negative impact on the environment due to”
Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications (2017, background): “On the other hand, UAVs can act as mobile aerial base stations to provide reliable downlink and uplink communications for ground users and boost the capacity of wireless networks [1], [3], [6]–[10].”
Common Throughput Maximization in UAV-Enabled OFDMA Systems With Delay Consideration (2018, background): “Although the UAV trajectory design has been shown to signiﬁcantly enhance the throughput of various wireless communication systems such as for mobile relaying channel [13], [15], multiple access channel (MAC) and broadcast channel (BC) [17], [21], [22], interference channel (IFC) [14], [18], and wir”
Ultra Reliable UAV Communication Using Altitude and Cooperation Diversity (2017, methodology): “) link reliability. In [9] the optimal placement of UAVs, acting as relays, is studied without considering the impact of altitude on its coverage range. This issue is addressed in recent efforts [10]–[12], where the authors show the dependency of the network performance on the altitude of UAVs. In [”
UAV Communications for 5G and Beyond: Recent Advances and Future Trends (2019, background): “[107] conducted the first attempt on providing a comprehensive performance analysis to evaluate the coexistence of UAV and D2D in terms of differ-”
Energy Minimization for Wireless Communication With Rotary-Wing UAV (2018, neutral): “Specifically, βk(t) can be written as [38]”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, methodology): “1) Static UAV Platform: xx Deterministic modelling of UAV location: In this case, the number as well as locations of UAVs are deterministic and known a priori [41,48,91]–[93], whereas their associated ground nodes could be modelled either deterministically or stochastically.”
Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach (2018, background): “and cost-effective network access to regions poorly covered by terrestrial networks [3].”
UAV-Assisted Heterogeneous Networks for Capacity Enhancement (2016, background): “Capacity refers to data rate transmission towards ground users, whereas delays refer to latency of data transmission [4].”
Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale (2018, background): “In this case, UAVs can enable a reliable and low-latency wireless backhaul connectivity for ground networks [3], [57], [58].”
UAV Trajectory Optimization for Data Offloading at the Edge of Multiple Cells (2018, neutral): “In [11], the sum rate was effectively maximized by Mozaffari et al.”
Energy-Efficient Data Collection in UAV Enabled Wireless Sensor Network (2017, background): “minals can be efficiently exploited in various UAV-enabled wireless networks for performance enhancement by properly designing the UAV’s trajectory [3]–[5].”
Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail (2018, methodology): “In our future work, we will extend the current framework to other use cases such as autonomous aerial vehicles (UAVs) [24], [25] and industrial control [26].”
On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks (2016, methodology): “In [3] adding an unmanned aerial vehicle (UAV) to a device-to-device (D2D) network is investigated and the average coverage probability and the sumrate for the users is derived as a function of UAV altitude and number of D2D users.”
Downlink Coverage Analysis for a Finite 3-D Wireless Network of Unmanned Aerial Vehicles (2017, background): “One relevant prior art is [20], which studies the co-existence of a device-to-device (D2D) communication network with a single UAV.”