666 research outputs found
3D Placement of an Unmanned Aerial Vehicle Base Station (UAV-BS) for Energy-Efficient Maximal Coverage
Unmanned Aerial Vehicle mounted base stations (UAV-BSs) can provide wireless
services in a variety of scenarios. In this letter, we propose an optimal
placement algorithm for UAV-BSs that maximizes the number of covered users
using the minimum transmit power. We decouple the UAV-BS deployment problem in
the vertical and horizontal dimensions without any loss of optimality.
Furthermore, we model the UAV-BS deployment in the horizontal dimension as a
circle placement problem and a smallest enclosing circle problem. Simulations
are conducted to evaluate the performance of the proposed method for different
spatial distributions of the users
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
Deployment Strategies of Multiple Aerial BSs for User Coverage and Power Efficiency Maximization
Unmanned aerial vehicle (UAV) based aerial base stations (BSs) can provide
rapid communication services to ground users and are thus promising for future
communication systems. In this paper, we consider a scenario where no
functional terrestrial BSs are available and the aim is deploying multiple
aerial BSs to cover a maximum number of users within a certain target area. To
this end, we first propose a naive successive deployment method, which converts
the non-convex constraints in the involved optimization into a combination of
linear constraints through geometrical relaxation. Then we investigate a
deployment method based on K-means clustering. The method divides the target
area into K convex subareas, where within each subarea, a mixed integer
non-linear problem (MINLP) is solved. An iterative power efficient technique is
further proposed to improve coverage probability with reduced power. Finally,
we propose a robust technique for compensating the loss of coverage probability
in the existence of inaccurate user location information (ULI). Our simulation
results show that, the proposed techniques achieve an up to 30% higher coverage
probability when users are not distributed uniformly. In addition, the proposed
simultaneous deployment techniques, especially the one using iterative
algorithm improve power-efficiency by up to 15% compared to the benchmark
circle packing theory
Resource Allocation for UAV Assisted Wireless Networks with QoS Constraints
For crowded and hotspot area, unmanned aerial vehicles (UAVs) are usually
deployed to increase the coverage rate. In the considered model, there are
three types of services for UAV assisted communication: control message,
non-realtime communication, and real-time communication, which can cover most
of the actual demands of users in a UAV assisted communication system. A
bandwidth allocation problem is considered to minimize the total energy
consumption of this system while satisfying the requirements. Two techniques
are introduced to enhance the performance of the system. The first method is to
categorize the ground users into multiple user groups and offer each group a
unique RF channel with different bandwidth. The second method is to deploy more
than one UAVs in the system. Bandwidth optimization in each scheme is proved to
be a convex problem. Simulation results show the superiority of the proposed
schemes in terms of energy consumption.Comment: Submitted to IEEE WCNC 202
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