22/06/2020

Optimally resilient codes for list-decoding from insertions and deletions

Venkatesan Guruswami, Bernhard Haeupler, Amirbehshad Shahrasbi

Keywords: List Decoding, Error Resilience, Coding for Insertions and Deletions

Abstract: We give a complete answer to the following basic question: ”What is the maximal fraction of deletions or insertions tolerable by q-ary list-decodable codes with non-vanishing information rate?” This question has been open even for binary codes, including the restriction to the binary insertion-only setting, where the best-known result was that a γ≤ 0.707 fraction of insertions is tolerable by some binary code family. For any desired є>0, we construct a family of binary codes of positive rate which can be efficiently list-decoded from any combination of γ fraction of insertions and δ fraction of deletions as long as γ+2δ≤ 1−ε. On the other hand, for any γ, δ with γ+2δ=1 list-decoding is impossible. Our result thus precisely characterizes the feasibility region of binary list-decodable codes for insertions and deletions. We further generalize our result to codes over any finite alphabet of size q. Surprisingly, our work reveals that the feasibility region for q>2 is not the natural generalization of the binary bound above. We provide tight upper and lower bounds that precisely pin down the feasibility region, which turns out to have a (q−1)-piece-wise linear boundary whose q corner-points lie on a quadratic curve. The main technical work in our results is proving the existence of code families of sufficiently large size with good list-decoding properties for any combination of δ,γ within the claimed feasibility region. We achieve this via an intricate analysis of codes introduced by [Bukh, Ma; SIAM J. Discrete Math; 2014]. Finally, we give a simple yet powerful concatenation scheme for list-decodable insertion-deletion codes which transforms any such (non-efficient) code family (with information rate zero) into an efficiently decodable code family with constant rate.

 0
 0
 0
 0
This is an embedded video. Talk and the respective paper are published at STOC 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd

Similar Papers