Optimally resilient codes for list-decoding from insertions and deletions.

IEEE Transactions on Information Theory(2020)

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摘要
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 gamma <= 0.707 fraction of insertions is tolerable by some binary code family. For any desired epsilon > 0, we construct a family of binary codes of positive rate which can be efficiently list-decoded from any combination of gamma fraction of insertions and (5 fraction of deletions as long as gamma + 2 delta <= 1 - epsilon. On the other hand, for any gamma, delta with gamma + 2 delta = 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 (5, y 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 vanishing information rate) into an efficiently decodable code family with constant rate.
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关键词
Coding for insertions and deletions, list decoding, error resilience
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