Service Quality Improvement of Mobile Users in Vehicular Environment by Mobile Femtocell Network Deployment
October 05, 2018 Β· Declared Dead Β· π Information and Communication Technology Convergence
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mostafa Zaman Chowdhurya, Seung Que Leeb, Byung Han Rub, Namhoon Parkb, Yeong Min Jang
arXiv ID
1810.02488
Category
cs.NI: Networking & Internet
Citations
47
Venue
Information and Communication Technology Convergence
Last Checked
6 months ago
Abstract
The femto-access-point (FAP), a low power small cellular base station provides better signal quality for the indoor users. The mobile users in the vehicular environment suffer for the low quality signal from the outside wireless networks. The deployment of femtocells in the vehicular environment can solve the low level signal-to-noise plus interference problem. In this paper, we propose new application of the femtocell technology. The femtocells are deployed in the vehicular environment. Short distance between the user and the FAP provides better signal quality. The inside FAPs are connected to the core network through the outside macrocellular networks or the satellite networks. One stronger transceiver is installed at outside the vehicle. This transceiver is connected to FAPs using wired connection and to macrocellular or the satellite access networks through wireless link. The capacity and the outage probability are analyzed. The simulation results show that the proposed mobile femtocell deployment significantly enhances the service quality of mobile users in the vehicular environment.
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