Latency Aware Drone Base Station Placement in Heterogeneous Networks
September 22, 2017 Β· Declared Dead Β· π Global Communications Conference
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Xiang Sun, Nirwan Ansari
arXiv ID
1710.01126
Category
cs.NI: Networking & Internet
Citations
44
Venue
Global Communications Conference
Last Checked
6 months ago
Abstract
Different from traditional static small cells, Drone Base Stations (DBSs) exhibit their own advantages, i.e., faster and cheaper to deploy, more flexibly reconfigured, and likely to have better communications channels owing to the presence of short-range line-of-sight links. Thus, applying DBSs into the cellular network has great potential to increase the throughput of the network and improve Quality of Service (QoS) of Mobile Users (MUs). In this paper, we focus on how to place the DBS (i.e., jointly determining the location and the association coverage of a DBS) in order to improve the QoS in terms of minimizing the total average latency ratio of MUs by considering the energy capacity limitation of the DBS. We formulate the DBS placement problem as an optimization problem and design a Latency aware dronE bAse station Placement (LEAP) algorithm to solve it efficiently. The performance of LEAP is demonstrated via simulations as compared to other two baseline methods.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Networking & Internet
R.I.P.
π»
Ghosted
π
π
The Cartographer
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
π
π
The Cartographer
A Survey of Indoor Localization Systems and Technologies
R.I.P.
π»
Ghosted
Survey of Important Issues in UAV Communication Networks
π
π
The Cartographer
Network Function Virtualization: State-of-the-art and Research Challenges
π
π
The Cartographer
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted