A Sequence Construction of Cyclic Codes over Finite Fields
November 20, 2016 Β· Declared Dead Β· π Cryptography and Communications
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Cunsheng Ding
arXiv ID
1611.06487
Category
cs.IT: Information Theory
Citations
16
Venue
Cryptography and Communications
Last Checked
4 months ago
Abstract
Cyclic codes over finite fields are widely implemented in data storage systems, communication systems, and consumer electronics, as they have very efficient encoding and decoding algorithms. They are also important in theory, as they are closely connected to several areas in mathematics. There are a few fundamental ways of constructing all cyclic codes over finite fields, including the generator matrix approach, the generator polynomial approach, the generating idempotent approach, and the $q$-polynomial approach. Another one is a sequence approach, which has been intensively investigated in the past decade. The objective of this paper is to survey the progress in the past decade in this direction.
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