2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | M.8.1 | ||
| Paper Title | Syndrome Compression for Optimal Redundancy Codes | ||
| Authors | Jin Sima, California Institute of Technology, United States; Ryan Gabrys, University of California San Diego, United States; Jehoshua Bruck, California Institute of Technology, United States | ||
| Session | M.8: Insertion Deletion Substitution Codes II | ||
| Presentation | Lecture | ||
| Track | Coding for Storage and Memories | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | We introduce a general technique that we call syndrome compression, for designing low-redundancy error correcting codes. The technique allows us to boost the redundancy efficiency of hash/labeling-based codes by further compressing the labeling. We apply syndrome compression to different types of adversarial deletion channels and present code constructions that correct up to a constant number of errors. Our code constructions achieve the redundancy of twice the Gilbert-Varshamov bound, which improve upon the state of art for these channels. The encoding/decoding complexity of our constructions is of order equal to the size of the corresponding deletion balls, namely, it is polynomial in the code length. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
