421 research outputs found
On the Proximity Factors of Lattice Reduction-Aided Decoding
Lattice reduction-aided decoding features reduced decoding complexity and
near-optimum performance in multi-input multi-output communications. In this
paper, a quantitative analysis of lattice reduction-aided decoding is
presented. To this aim, the proximity factors are defined to measure the
worst-case losses in distances relative to closest point search (in an infinite
lattice). Upper bounds on the proximity factors are derived, which are
functions of the dimension of the lattice alone. The study is then extended
to the dual-basis reduction. It is found that the bounds for dual basis
reduction may be smaller. Reasonably good bounds are derived in many cases. The
constant bounds on proximity factors not only imply the same diversity order in
fading channels, but also relate the error probabilities of (infinite) lattice
decoding and lattice reduction-aided decoding.Comment: remove redundant figure
Decoding by Embedding: Correct Decoding Radius and DMT Optimality
The closest vector problem (CVP) and shortest (nonzero) vector problem (SVP)
are the core algorithmic problems on Euclidean lattices. They are central to
the applications of lattices in many problems of communications and
cryptography. Kannan's \emph{embedding technique} is a powerful technique for
solving the approximate CVP, yet its remarkable practical performance is not
well understood. In this paper, the embedding technique is analyzed from a
\emph{bounded distance decoding} (BDD) viewpoint. We present two complementary
analyses of the embedding technique: We establish a reduction from BDD to
Hermite SVP (via unique SVP), which can be used along with any Hermite SVP
solver (including, among others, the Lenstra, Lenstra and Lov\'asz (LLL)
algorithm), and show that, in the special case of LLL, it performs at least as
well as Babai's nearest plane algorithm (LLL-aided SIC). The former analysis
helps to explain the folklore practical observation that unique SVP is easier
than standard approximate SVP. It is proven that when the LLL algorithm is
employed, the embedding technique can solve the CVP provided that the noise
norm is smaller than a decoding radius , where
is the minimum distance of the lattice, and . This
substantially improves the previously best known correct decoding bound . Focusing on the applications of BDD to decoding of
multiple-input multiple-output (MIMO) systems, we also prove that BDD of the
regularized lattice is optimal in terms of the diversity-multiplexing gain
tradeoff (DMT), and propose practical variants of embedding decoding which
require no knowledge of the minimum distance of the lattice and/or further
improve the error performance.Comment: To appear in IEEE Transactions on Information Theor
Classification of eight dimensional perfect forms
In this paper, we classify the perfect lattices in dimension 8. There are
10916 of them. Our classification heavily relies on exploiting symmetry in
polyhedral computations. Here we describe algorithms making the classification
possible.Comment: 14 page
Integer-Forcing MIMO Linear Receivers Based on Lattice Reduction
A new architecture called integer-forcing (IF) linear receiver has been
recently proposed for multiple-input multiple-output (MIMO) fading channels,
wherein an appropriate integer linear combination of the received symbols has
to be computed as a part of the decoding process. In this paper, we propose a
method based on Hermite-Korkine-Zolotareff (HKZ) and Minkowski lattice basis
reduction algorithms to obtain the integer coefficients for the IF receiver. We
show that the proposed method provides a lower bound on the ergodic rate, and
achieves the full receive diversity. Suitability of complex
Lenstra-Lenstra-Lovasz (LLL) lattice reduction algorithm (CLLL) to solve the
problem is also investigated. Furthermore, we establish the connection between
the proposed IF linear receivers and lattice reduction-aided MIMO detectors
(with equivalent complexity), and point out the advantages of the former class
of receivers over the latter. For the and MIMO
channels, we compare the coded-block error rate and bit error rate of the
proposed approach with that of other linear receivers. Simulation results show
that the proposed approach outperforms the zero-forcing (ZF) receiver, minimum
mean square error (MMSE) receiver, and the lattice reduction-aided MIMO
detectors.Comment: 9 figures and 11 pages. Modified the title, abstract and some parts
of the paper. Major change from v1: Added new results on applicability of the
CLLL reductio
Tensor-based trapdoors for CVP and their application to public key cryptography
We propose two trapdoors for the Closest-Vector-Problem in lattices (CVP) related to the lattice tensor product. Using these trapdoors we set up a lattice-based cryptosystem which resembles to the McEliece scheme
Dual lattice attacks for closest vector problems (with preprocessing)
The dual attack has long been considered a relevant attack on lattice-based cryptographic schemes relying on the hardness of learning with errors (LWE) and its structured variants. As solving LWE corresponds to finding a nearest point on a lattice, one may naturally wonder how efficient this dual approach is for solving more general closest vector problems, such as the classical closest vector problem (CVP), the variants bounded distance decoding (BDD) and approximate CVP, and preprocessing versions of these problems. While primal, sieving-based solutions to these problems (with preprocessing) were recently studied in a series of works on approximate Voronoi cells, for the dual attack no such overview exists, especially for problems with preprocessing. With one of the take-away messages of the approximate Voronoi cell line of work being that primal attacks work well for approximate CVP(P) but scale poorly for BDD(P), one may wonder if the dual attack suffers the same drawbacks, or if it is a better method for solving BDD(P).
In this work we provide an overview of cost estimates for dual algorithms for solving these \u27\u27classical\u27\u27 closest lattice vector problems. Heuristically we expect to solve the search version of average-case CVPP in time and space . For the distinguishing version of average-case CVPP, where we wish to distinguish between random targets and targets planted at distance approximately the Gaussian heuristic from the lattice, we obtain the same complexity in the single-target model, and we obtain query time and space complexities of in the multi-target setting, where we are given a large number of targets from either target distribution. This suggests an inequivalence between distinguishing and searching, as we do not expect a similar improvement in the multi-target setting to hold for search-CVPP. We analyze three slightly different decoders, both for distinguishing and searching, and experimentally obtain concrete cost estimates for the dual attack in dimensions to , which confirm our heuristic assumptions, and show that the hidden order terms in the asymptotic estimates are quite small.
Our main take-away message is that the dual attack appears to mirror the approximate Voronoi cell line of work -- whereas using approximate Voronoi cells works well for approximate CVP(P) but scales poorly for BDD(P), the dual approach scales well for BDD(P) instances but performs poorly on approximate CVP(P)
Recursive lattice reduction -- A framework for finding short lattice vectors
We propose a new framework called recursive lattice reduction for finding
short non-zero vectors in a lattice or for finding dense sublattices of a
lattice. At a high level, the framework works by recursively searching for
dense sublattices of dense sublattices (or their duals). Eventually, the
procedure encounters a recursive call on a lattice with
relatively low rank , at which point we simply use a known algorithm to find
a short non-zero vector in . We view our framework as
complementary to basis reduction algorithms, which similarly work to reduce an
-dimensional lattice problem with some approximation factor to an
exact lattice problem in dimension , with a tradeoff between ,
, and . Our framework provides an alternative and arguably simpler
perspective, which in particular can be described without explicitly
referencing any specific basis of the lattice, Gram-Schmidt vectors, or even
projection (though implementations of algorithms in this framework will likely
make use of such things). We present a number of specific instantiations of our
framework. Our main concrete result is a reduction that matches the tradeoff
between , , and achieved by the best-known basis reduction
algorithms (in terms of the Hermite factor, up to low-order terms) across all
parameter regimes. In fact, this reduction also can be used to find dense
sublattices with any rank satisfying ,
using only an oracle for SVP (or even just Hermite SVP) in dimensions,
which is itself a novel result (as far as the authors know). We also show a
very simple reduction that achieves the same tradeoff in quasipolynomial time.
Finally, we present an automated approach for searching for algorithms in this
framework that (provably) achieve better approximations with fewer oracle
calls
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