Backhaul-Aware Resource Allocation and Optimum Placement for UAV-Assisted Wireless Communication Network

Driven by its agile maneuverability and deployment, the unmanned aerial vehicle (UAV) becomes a potential enabler of the terrestrial networks. In this paper, we consider downlink communications in a UAV-assisted wireless communication network, where a multi-antenna UAV assists the ground base statio...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Electronics (Basel) Ročník 9; číslo 9; s. 1397
Hlavní autori: Xue, Yishi, Xu, Bo, Xia, Wenchao, Zhang, Jun, Zhu, Hongbo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.09.2020
Predmet:
Algorithms
Antennas
Approximation
Communications networks
Downlinking
Equivalence
Ground stations
Maneuverability
Matrix theory
Optimization
Placement
Radio equipment
Random variables
Resource allocation
Spectrum allocation
Unmanned aerial vehicles
Wireless communications
Wireless networks
ISSN:2079-9292, 2079-9292
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Driven by its agile maneuverability and deployment, the unmanned aerial vehicle (UAV) becomes a potential enabler of the terrestrial networks. In this paper, we consider downlink communications in a UAV-assisted wireless communication network, where a multi-antenna UAV assists the ground base station (GBS) to forward signals to multiple user equipments (UEs). The UAV is associated with the GBS through in-band wireless backhaul, which shares the spectrum resource with the access links between UEs and the UAV. The optimization problem is formulated to maximize the downlink ergodic sum-rate by jointly optimizing UAV placement, spectrum resource allocation and transmit power matrix of the UAV. The deterministic equivalents of UE’s achievable rate and backhaul capacity are first derived by utilizing large-dimensional random matrix theory, in which, only the slowly varying large-scale channel state information is required. An approximation problem of the joint optimization problem is then introduced based on the deterministic equivalents. Finally, an algorithm is proposed to obtain the optimal solution of the approximate problem. Simulation results are provided to validate the accuracy of the deterministic equivalents, and the effectiveness of the proposed method.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics9091397