3,003 research outputs found
Rank Minimization over Finite Fields: Fundamental Limits and Coding-Theoretic Interpretations
This paper establishes information-theoretic limits in estimating a finite
field low-rank matrix given random linear measurements of it. These linear
measurements are obtained by taking inner products of the low-rank matrix with
random sensing matrices. Necessary and sufficient conditions on the number of
measurements required are provided. It is shown that these conditions are sharp
and the minimum-rank decoder is asymptotically optimal. The reliability
function of this decoder is also derived by appealing to de Caen's lower bound
on the probability of a union. The sufficient condition also holds when the
sensing matrices are sparse - a scenario that may be amenable to efficient
decoding. More precisely, it is shown that if the n\times n-sensing matrices
contain, on average, \Omega(nlog n) entries, the number of measurements
required is the same as that when the sensing matrices are dense and contain
entries drawn uniformly at random from the field. Analogies are drawn between
the above results and rank-metric codes in the coding theory literature. In
fact, we are also strongly motivated by understanding when minimum rank
distance decoding of random rank-metric codes succeeds. To this end, we derive
distance properties of equiprobable and sparse rank-metric codes. These
distance properties provide a precise geometric interpretation of the fact that
the sparse ensemble requires as few measurements as the dense one. Finally, we
provide a non-exhaustive procedure to search for the unknown low-rank matrix.Comment: Accepted to the IEEE Transactions on Information Theory; Presented at
IEEE International Symposium on Information Theory (ISIT) 201
List and Probabilistic Unique Decoding of Folded Subspace Codes
A new class of folded subspace codes for noncoherent network coding is
presented. The codes can correct insertions and deletions beyond the unique
decoding radius for any code rate . An efficient interpolation-based
decoding algorithm for this code construction is given which allows to correct
insertions and deletions up to the normalized radius ,
where is the folding parameter and is a decoding parameter. The
algorithm serves as a list decoder or as a probabilistic unique decoder that
outputs a unique solution with high probability. An upper bound on the average
list size of (folded) subspace codes and on the decoding failure probability is
derived. A major benefit of the decoding scheme is that it enables
probabilistic unique decoding up to the list decoding radius.Comment: 6 pages, 1 figure, accepted for ISIT 201
List and Unique Error-Erasure Decoding of Interleaved Gabidulin Codes with Interpolation Techniques
A new interpolation-based decoding principle for interleaved Gabidulin codes
is presented. The approach consists of two steps: First, a multi-variate
linearized polynomial is constructed which interpolates the coefficients of the
received word and second, the roots of this polynomial have to be found. Due to
the specific structure of the interpolation polynomial, both steps
(interpolation and root-finding) can be accomplished by solving a linear system
of equations. This decoding principle can be applied as a list decoding
algorithm (where the list size is not necessarily bounded polynomially) as well
as an efficient probabilistic unique decoding algorithm. For the unique
decoder, we show a connection to known unique decoding approaches and give an
upper bound on the failure probability. Finally, we generalize our approach to
incorporate not only errors, but also row and column erasures.Comment: accepted for Designs, Codes and Cryptography; presented in part at
WCC 2013, Bergen, Norwa
On Linear Operator Channels over Finite Fields
Motivated by linear network coding, communication channels perform linear
operation over finite fields, namely linear operator channels (LOCs), are
studied in this paper. For such a channel, its output vector is a linear
transform of its input vector, and the transformation matrix is randomly and
independently generated. The transformation matrix is assumed to remain
constant for every T input vectors and to be unknown to both the transmitter
and the receiver. There are NO constraints on the distribution of the
transformation matrix and the field size.
Specifically, the optimality of subspace coding over LOCs is investigated. A
lower bound on the maximum achievable rate of subspace coding is obtained and
it is shown to be tight for some cases. The maximum achievable rate of
constant-dimensional subspace coding is characterized and the loss of rate
incurred by using constant-dimensional subspace coding is insignificant.
The maximum achievable rate of channel training is close to the lower bound
on the maximum achievable rate of subspace coding. Two coding approaches based
on channel training are proposed and their performances are evaluated. Our
first approach makes use of rank-metric codes and its optimality depends on the
existence of maximum rank distance codes. Our second approach applies linear
coding and it can achieve the maximum achievable rate of channel training. Our
code designs require only the knowledge of the expectation of the rank of the
transformation matrix. The second scheme can also be realized ratelessly
without a priori knowledge of the channel statistics.Comment: 53 pages, 3 figures, submitted to IEEE Transaction on Information
Theor
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