49,834 research outputs found
Communication over Finite-Chain-Ring Matrix Channels
Though network coding is traditionally performed over finite fields, recent
work on nested-lattice-based network coding suggests that, by allowing network
coding over certain finite rings, more efficient physical-layer network coding
schemes can be constructed. This paper considers the problem of communication
over a finite-ring matrix channel , where is the channel
input, is the channel output, is random error, and and are
random transfer matrices. Tight capacity results are obtained and simple
polynomial-complexity capacity-achieving coding schemes are provided under the
assumption that is uniform over all full-rank matrices and is uniform
over all rank- matrices, extending the work of Silva, Kschischang and
K\"{o}tter (2010), who handled the case of finite fields. This extension is
based on several new results, which may be of independent interest, that
generalize concepts and methods from matrices over finite fields to matrices
over finite chain rings.Comment: Submitted to IEEE Transactions on Information Theory, April 2013.
Revised version submitted in Feb. 2014. Final version submitted in June 201
An Algebraic Approach for Decoding Spread Codes
In this paper we study spread codes: a family of constant-dimension codes for
random linear network coding. In other words, the codewords are full-rank
matrices of size (k x n) with entries in a finite field F_q. Spread codes are a
family of optimal codes with maximal minimum distance. We give a
minimum-distance decoding algorithm which requires O((n-k)k^3) operations over
an extension field F_{q^k}. Our algorithm is more efficient than the previous
ones in the literature, when the dimension k of the codewords is small with
respect to n. The decoding algorithm takes advantage of the algebraic structure
of the code, and it uses original results on minors of a matrix and on the
factorization of polynomials over finite fields
Good Random Matrices over Finite Fields
The random matrix uniformly distributed over the set of all m-by-n matrices
over a finite field plays an important role in many branches of information
theory. In this paper a generalization of this random matrix, called k-good
random matrices, is studied. It is shown that a k-good random m-by-n matrix
with a distribution of minimum support size is uniformly distributed over a
maximum-rank-distance (MRD) code of minimum rank distance min{m,n}-k+1, and
vice versa. Further examples of k-good random matrices are derived from
homogeneous weights on matrix modules. Several applications of k-good random
matrices are given, establishing links with some well-known combinatorial
problems. Finally, the related combinatorial concept of a k-dense set of m-by-n
matrices is studied, identifying such sets as blocking sets with respect to
(m-k)-dimensional flats in a certain m-by-n matrix geometry and determining
their minimum size in special cases.Comment: 25 pages, publishe
Stein's method and the rank distribution of random matrices over finite fields
With the distribution of minus the rank of a matrix
chosen uniformly from the collection of all matrices over the
finite field of size , and the
distributional limit of as , we apply
Stein's method to prove the total variation bound
.
In addition, we obtain similar sharp results for the rank distributions of
symmetric, symmetric with zero diagonal, skew symmetric, skew centrosymmetric
and Hermitian matrices.Comment: Published at http://dx.doi.org/10.1214/13-AOP889 in the Annals of
Probability (http://www.imstat.org/aop/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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
Computational linear algebra over finite fields
We present here algorithms for efficient computation of linear algebra
problems over finite fields
High-Dimensional Bayesian Geostatistics
With the growing capabilities of Geographic Information Systems (GIS) and
user-friendly software, statisticians today routinely encounter geographically
referenced data containing observations from a large number of spatial
locations and time points. Over the last decade, hierarchical spatiotemporal
process models have become widely deployed statistical tools for researchers to
better understand the complex nature of spatial and temporal variability.
However, fitting hierarchical spatiotemporal models often involves expensive
matrix computations with complexity increasing in cubic order for the number of
spatial locations and temporal points. This renders such models unfeasible for
large data sets. This article offers a focused review of two methods for
constructing well-defined highly scalable spatiotemporal stochastic processes.
Both these processes can be used as "priors" for spatiotemporal random fields.
The first approach constructs a low-rank process operating on a
lower-dimensional subspace. The second approach constructs a Nearest-Neighbor
Gaussian Process (NNGP) that ensures sparse precision matrices for its finite
realizations. Both processes can be exploited as a scalable prior embedded
within a rich hierarchical modeling framework to deliver full Bayesian
inference. These approaches can be described as model-based solutions for big
spatiotemporal datasets. The models ensure that the algorithmic complexity has
floating point operations (flops), where the number of spatial
locations (per iteration). We compare these methods and provide some insight
into their methodological underpinnings
A heuristic for boundedness of ranks of elliptic curves
We present a heuristic that suggests that ranks of elliptic curves over the
rationals are bounded. In fact, it suggests that there are only finitely many
elliptic curves of rank greater than 21. Our heuristic is based on modeling the
ranks and Shafarevich-Tate groups of elliptic curves simultaneously, and relies
on a theorem counting alternating integer matrices of specified rank. We also
discuss analogues for elliptic curves over other global fields.Comment: 41 pages, typos fixed in torsion table in section
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