An Explicit Rate-Optimal Streaming Code for Channels with Burst and Arbitrary Erasures
April 11, 2019 Β· Declared Dead Β· π Information Theory Workshop
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Elad Domanovitz, Silas L. Fong, Ashish Khisti
arXiv ID
1904.06212
Category
cs.IT: Information Theory
Citations
42
Venue
Information Theory Workshop
Last Checked
6 months ago
Abstract
This paper considers the transmission of an infinite sequence of messages (a streaming source) over a packet erasure channel, where every source message must be recovered perfectly at the destination subject to a fixed decoding delay. While the capacity of a channel that introduces only bursts of erasures is well known, only recently, the capacity of a channel with either one burst of erasures or multiple arbitrary erasures in any fixed-sized sliding window has been established. However, the codes shown to achieve this capacity are either non-explicit constructions (proven to exist) or explicit constructions that require large field size that scales exponentially with the delay. This work describes an explicit rate-optimal construction for admissible channel and delay parameters over a field size that scales only quadratically with the delay.
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