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Linear Size Optimal q-ary Constant-Weight Codes and Constant-Composition Codes
An optimal constant-composition or constant-weight code of weight has
linear size if and only if its distance is at least . When , the determination of the exact size of such a constant-composition or
constant-weight code is trivial, but the case of has been solved
previously only for binary and ternary constant-composition and constant-weight
codes, and for some sporadic instances.
This paper provides a construction for quasicyclic optimal
constant-composition and constant-weight codes of weight and distance
based on a new generalization of difference triangle sets. As a result,
the sizes of optimal constant-composition codes and optimal constant-weight
codes of weight and distance are determined for all such codes of
sufficiently large lengths. This solves an open problem of Etzion.
The sizes of optimal constant-composition codes of weight and distance
are also determined for all , except in two cases.Comment: 12 page
Combinatorial limitations of average-radius list-decoding
We study certain combinatorial aspects of list-decoding, motivated by the
exponential gap between the known upper bound (of ) and lower
bound (of ) for the list-size needed to decode up to
radius with rate away from capacity, i.e., 1-\h(p)-\gamma (here
and ). Our main result is the following:
We prove that in any binary code of rate
1-\h(p)-\gamma, there must exist a set of
codewords such that the average distance of the
points in from their centroid is at most . In other words,
there must exist codewords with low "average
radius." The standard notion of list-decoding corresponds to working with the
maximum distance of a collection of codewords from a center instead of average
distance. The average-radius form is in itself quite natural and is implied by
the classical Johnson bound.
The remaining results concern the standard notion of list-decoding, and help
clarify the combinatorial landscape of list-decoding:
1. We give a short simple proof, over all fixed alphabets, of the
above-mentioned lower bound. Earlier, this bound
followed from a complicated, more general result of Blinovsky.
2. We show that one {\em cannot} improve the
lower bound via techniques based on identifying the zero-rate regime for list
decoding of constant-weight codes.
3. We show a "reverse connection" showing that constant-weight codes for list
decoding imply general codes for list decoding with higher rate.
4. We give simple second moment based proofs of tight (up to constant
factors) lower bounds on the list-size needed for list decoding random codes
and random linear codes from errors as well as erasures.Comment: 28 pages. Extended abstract in RANDOM 201
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