Minimizing The Age of Information: NOMA or OMA?
January 10, 2019 Β· Declared Dead Β· π Conference on Computer Communications Workshops
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Ali Maatouk, Mohamad Assaad, Anthony Ephremides
arXiv ID
1901.03020
Category
cs.IT: Information Theory
Citations
110
Venue
Conference on Computer Communications Workshops
Last Checked
4 months ago
Abstract
In this paper, we examine the potentials of Non- Orthogonal Multiple Access (NOMA), currently rivaling Orthogonal Multiple Access (OMA) in 3rd Generation Partnership Project (3GPP) standardization for future 5G networks Machine Type Communications (MTC), in the framework of minimizing the average Age of Information (AoI). By leveraging the notion of Stochastic Hybrid Systems (SHS), we find the total average AoI of the network in simple NOMA and conventional OMA environments. Armed with this, we provide a comparison between the two schemes in terms of average AoI. Interestingly, it will be shown that even when NOMA achieves better spectral efficiency in comparison to OMA, this does not necessarily translates into a lower average AoI in the network.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Information Theory
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems
R.I.P.
π»
Ghosted
Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network
π
π
The Cartographer
Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges
R.I.P.
π»
Ghosted
Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication
π
π
The Cartographer
An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems
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