Abstract
The Internet of Things (IoT) brings connectivity to a massive number of devices that demand energy-efficient solutions to deal with limited battery capacities, uplink-dominant traffic, and channel impairments. In this work, we explore the use of unmanned aerial vehicles (UAVs) equipped with configurable antennas as a flexible solution for serving low-power IoT networks. We formulate an optimization problem to set the position and antenna beamwidth of the UAV, and the transmit power of the IoT devices subject to average-signal-to-average-interference-plus-noise ratio ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\bar {\text {S}}\overline {\text {IN}}\text {R}$ </tex-math></inline-formula> ) Quality-of-Service (QoS) constraints. We minimize the worst case average energy consumption of the latter, thus targeting the fairest allocation of the energy resources. The problem is nonconvex and highly nonlinear; therefore, we reformulate it as a series of three geometric programs that can be solved iteratively. Results reveal the benefits of planning the network compared to a random deployment in terms of reducing the worst case average energy consumption. Furthermore, we show that the target <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\bar {\text {S}}\overline {\text {IN}}\text {R}$ </tex-math></inline-formula> is limited by the number of IoT devices, and highlight the dominant impact of the UAV hovering height when serving wider areas. Our proposed algorithm outperforms other optimization benchmarks in terms of minimizing the average energy consumption at the most energy-demanding IoT device, and convergence time.
Highlights
The fifth generation of cellular networks (5G) is introducing for the first time, in addition to the traditional human-centric broadband communication services, new service classes related to the Internet of Things (IoT) [1]
Genetic Algorithms Geometric Programming Internet of Things Interior-point methods Low-altitude platform Line of Sight Quality of Service average-Signal-to-average-Interference-plus-Noise Ratio Unmanned Aerial Vehicle inf Infimum sup Supremum ck Gk Lk O (·) pk β, ψ θB η ζ K γk Activation probability of device k Antenna gain seen by device k Path loss between the Unmanned Aerial Vehicles (UAVs) and device k Order of the function Transmit power of device k Propagation parameters UAV’s antenna half beamwidth Path loss coefficient Convergence parameter Number of IoT devicesSINR of device k
We proposed a Geometric Programming (GP)-based algorithm for minimizing the worst-case average energy consumption of IoT devices served by a hovering UAV
Summary
It is desirable to have a BS that can dynamically change its coverage based on the position and traffic pattern of the IoT devices This goal can be achieved by using aerial BSs, such as Unmanned Aerial vehicles (UAVs) [11]. By equipping the UAVs with reconfigurable antennas [17], more degrees of freedom could be attained since it is possible to adjust the beam footprint of the UAV by means of electrical, optical, mechanical, and material change techniques to boost even more the coverage with QoS guarantees 2 These features are precisely exploited in our work to reduce the worst-case average energy consumption of the IoT devices
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