434 research outputs found
It'll probably work out: improved list-decoding through random operations
In this work, we introduce a framework to study the effect of random
operations on the combinatorial list-decodability of a code. The operations we
consider correspond to row and column operations on the matrix obtained from
the code by stacking the codewords together as columns. This captures many
natural transformations on codes, such as puncturing, folding, and taking
subcodes; we show that many such operations can improve the list-decoding
properties of a code. There are two main points to this. First, our goal is to
advance our (combinatorial) understanding of list-decodability, by
understanding what structure (or lack thereof) is necessary to obtain it.
Second, we use our more general results to obtain a few interesting corollaries
for list decoding:
(1) We show the existence of binary codes that are combinatorially
list-decodable from fraction of errors with optimal rate
that can be encoded in linear time.
(2) We show that any code with relative distance, when randomly
folded, is combinatorially list-decodable fraction of errors with
high probability. This formalizes the intuition for why the folding operation
has been successful in obtaining codes with optimal list decoding parameters;
previously, all arguments used algebraic methods and worked only with specific
codes.
(3) We show that any code which is list-decodable with suboptimal list sizes
has many subcodes which have near-optimal list sizes, while retaining the error
correcting capabilities of the original code. This generalizes recent results
where subspace evasive sets have been used to reduce list sizes of codes that
achieve list decoding capacity
Linear-time list recovery of high-rate expander codes
We show that expander codes, when properly instantiated, are high-rate list
recoverable codes with linear-time list recovery algorithms. List recoverable
codes have been useful recently in constructing efficiently list-decodable
codes, as well as explicit constructions of matrices for compressive sensing
and group testing. Previous list recoverable codes with linear-time decoding
algorithms have all had rate at most 1/2; in contrast, our codes can have rate
for any . We can plug our high-rate codes into a
construction of Meir (2014) to obtain linear-time list recoverable codes of
arbitrary rates, which approach the optimal trade-off between the number of
non-trivial lists provided and the rate of the code. While list-recovery is
interesting on its own, our primary motivation is applications to
list-decoding. A slight strengthening of our result would implies linear-time
and optimally list-decodable codes for all rates, and our work is a step in the
direction of solving this important problem
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