Computer Science > Networking and Internet Architecture
[Submitted on 30 May 2022]
Title:On the impact of the physical layer model on the performance of D2D-offloading in vehicular environments
View PDFAbstract:Offloading data traffic from Infrastructure-to-Device (I2D) to Device-to-Device (D2D) communications is a powerful tool for reducing congestion, energy consumption, and spectrum usage of mobile cellular networks. Prior network-level studies on D2D data offloading focus on high level performance metrics as the offloading efficiency, and take into account the radio propagation aspects by using simplistic wireless channel models. We consider a D2D data offloading protocol tailored to highly dynamic scenarios as vehicular environments, and evaluate its performance focusing on physical layer aspects, like energy consumption and spectral efficiency. We do this by taking into account more realistic models of the wireless channel, with respect to the simplistic ones generally used in the previous studies. Our objective is twofold: first, to quantify the performance gain of the considered D2D offloading protocol with respect to a classic cellular network, based on I2D communications, in terms of energy consumption and spectral efficiency. Second, to show that using simplistic channel models may prevent to accurately evaluate the performance gain. Additionally, the use of more elaborated models allows to obtain insightful information on relevant system-level parameters settings, which would not be possible to obtain by using simple models. The considered channel models have been proposed and validated, in the recent years, through large-scale measurements campaigns.
Our results show that the considered protocol is able to achieve a reduction in the energy consumption of up to 35%, and an increase in the system spectral efficiency of 50%, with respect to the benchmark cellular system. The use of different channel models in evaluating these metrics may result, in the worst case, in a sixfold underestimation of the achieved improvement.
Submission history
From: Loreto Pescosolido [view email][v1] Mon, 30 May 2022 13:41:46 UTC (657 KB)
References & Citations
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.