482 research outputs found
Underlay Drone Cell for Temporary Events: Impact of Drone Height and Aerial Channel Environments
Providing seamless connection to a large number of devices is one of the
biggest challenges for the Internet of Things (IoT) networks. Using a drone as
an aerial base station (ABS) to provide coverage to devices or users on ground
is envisaged as a promising solution for IoT networks. In this paper, we
consider a communication network with an underlay ABS to provide coverage for a
temporary event, such as a sporting event or a concert in a stadium. Using
stochastic geometry, we propose a general analytical framework to compute the
uplink and downlink coverage probabilities for both the aerial and the
terrestrial cellular system. Our framework is valid for any aerial channel
model for which the probabilistic functions of line-of-sight (LOS) and
non-line-of-sight (NLOS) links are specified. The accuracy of the analytical
results is verified by Monte Carlo simulations considering two commonly adopted
aerial channel models. Our results show the non-trivial impact of the different
aerial channel environments (i.e., suburban, urban, dense urban and high-rise
urban) on the uplink and downlink coverage probabilities and provide design
guidelines for best ABS deployment height.Comment: This work is accepted to appear in IEEE Internet of Things Journal
Special Issue on UAV over IoT. Copyright may be transferred without notice,
after which this version may no longer be accessible. arXiv admin note: text
overlap with arXiv:1801.0594
Coverage and Rate Analysis for Unmanned Aerial Vehicle Base Stations with LoS/NLoS Propagation
The use of unmanned aerial vehicle base stations (UAV-BSs) as airborne base
stations has recently gained great attention. In this paper, we model a network
of UAV-BSs as a Poisson point process (PPP) operating at a certain altitude
above the ground users. We adopt an air-to-ground (A2G) channel model that
incorporates line-of-sight (LoS) and non-line-of-sight (NLoS) propagation.
Thus, UAV-BSs can be decomposed into two independent inhomogeneous PPPs. Under
the assumption that NLoS and LoS channels experience Rayleigh and Nakagami-m
fading, respectively, we derive approximations for the coverage probability and
average achievable rate, and show that these approximations match the
simulations with negligible errors. Numerical simulations have shown that the
coverage probability and average achievable rate decrease as the height of the
UAV-BSs increases
Echo State Learning for Wireless Virtual Reality Resource Allocation in UAV-enabled LTE-U Networks
In this paper, the problem of resource management is studied for a network of
wireless virtual reality (VR) users communicating using an unmanned aerial
vehicle (UAV)-enabled LTE-U network. In the studied model, the UAVs act as VR
control centers that collect tracking information from the VR users over the
wireless uplink and, then, send the constructed VR images to the VR users over
an LTE-U downlink. Therefore, resource allocation in such a UAV-enabled LTE-U
network must jointly consider the uplink and downlink links over both licensed
and unlicensed bands. In such a VR setting, the UAVs can dynamically adjust the
image quality and format of each VR image to change the data size of each VR
image, then meet the delay requirement. Therefore, resource allocation must
also take into account the image quality and format. This VR-centric resource
allocation problem is formulated as a noncooperative game that enables a joint
allocation of licensed and unlicensed spectrum bands, as well as a dynamic
adaptation of VR image quality and format. To solve this game, a learning
algorithm based on the machine learning tools of echo state networks (ESNs)
with leaky integrator neurons is proposed. Unlike conventional ESN based
learning algorithms that are suitable for discrete-time systems, the proposed
algorithm can dynamically adjust the update speed of the ESN's state and,
hence, it can enable the UAVs to learn the continuous dynamics of their
associated VR users. Simulation results show that the proposed algorithm
achieves up to 14% and 27.1% gains in terms of total VR QoE for all users
compared to Q-learning using LTE-U and Q-learning using LTE
Unmanned Aerial Vehicle with Underlaid Device-to-Device Communications: Performance and Tradeoffs
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.Comment: accepted in the IEEE Transactions on Wireless Communication
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