Decentralized Coded Caching with Distinct Cache Capacities
October 12, 2016 Β· Declared Dead Β· π Asilomar Conference on Signals, Systems and Computers
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mohammad Mohammadi Amiri, Qianqian Yang, Deniz GΓΌndΓΌz
arXiv ID
1610.03792
Category
cs.IT: Information Theory
Citations
36
Venue
Asilomar Conference on Signals, Systems and Computers
Last Checked
6 months ago
Abstract
Decentralized coded caching is studied for a content server with $N$ files, each of size $F$ bits, serving $K$ active users, each equipped with a cache of distinct capacity. It is assumed that the users' caches are filled in advance during the off-peak traffic period without the knowledge of the number of active users, their identities, or the particular demands. User demands are revealed during the peak traffic period, and are served simultaneously through an error-free shared link. A new decentralized coded caching scheme is proposed for this scenario, and it is shown to improve upon the state-of-the-art in terms of the required delivery rate over the shared link, when there are more users in the system than the number of files. Numerical results indicate that the improvement becomes more significant as the cache capacities of the users become more skewed.
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