학술논문

Unmanned aerial vehicle positioning and user equipment power allocation
Document Type
Conference
Source
2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) Communication Systems, Networks and Digital Signal Processing (CSNDSP), 2022 13th International Symposium on. :773-778 Jul, 2022
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Signal Processing and Analysis
Wireless communication
Runtime
Spectral efficiency
System performance
Signal processing algorithms
Switches
Autonomous aerial vehicles
Unmanned aerial vehicle (UAV)
Position optimization
Power allocation
Meta-heuristics (MHs)
Differential Evolution (DE)
Language
Abstract
In the near future, unmanned aerial vehicles (UAVs) will have enormous potential applications for next generation wireless communication systems, in which they can collaborate to serve several user equipment (UE) for communication purposes.Disaster scenarios are a relevant research topic, in which UAVs can aid establishing connections in circumstances where base stations (BS) may be inoperative.The resulting UAV positioning affects the overall spectral efficiency (SE) in each UAV-UE link. Moreover, UEs energy consumption must be optimized since the finite amount of energy available is one of the most significant limitations of these devices. Therefore, it is essential to determine the lowest power consumption necessary to guarantee a minimum SE throughput in a disaster location.In this paper, we investigate a cooperative meta-heuristic (MH) optimization algorithm for both the UAVs and UEs. We propose two parallel optimization approaches: one is the UAV search position process to find the best possible location to serve its pre-allocated UEs; the other is finding the lowest possible uplink (UL) power values for each user’s equipment. The preliminary results show that the Differential Evolution (DE) algorithm reaches good quality solutions in acceptable computation runtime.