918 research outputs found
The hardness of decoding linear codes with preprocessing
The problem of maximum-likelihood decoding of linear block codes is known to be hard. The fact that the problem remains hard even if the code is known in advance, and can be preprocessed for as long as desired in order to device a decoding algorithm, is shown. The hardness is based on the fact that existence of a polynomial-time algorithm implies that the polynomial hierarchy collapses. Thus, some linear block codes probably do not have an efficient decoder. The proof is based on results in complexity theory that relate uniform and nonuniform complexity classes
Maximum-likelihood decoding of Reed-Solomon Codes is NP-hard
Maximum-likelihood decoding is one of the central algorithmic problems in
coding theory. It has been known for over 25 years that maximum-likelihood
decoding of general linear codes is NP-hard. Nevertheless, it was so far
unknown whether maximum- likelihood decoding remains hard for any specific
family of codes with nontrivial algebraic structure. In this paper, we prove
that maximum-likelihood decoding is NP-hard for the family of Reed-Solomon
codes. We moreover show that maximum-likelihood decoding of Reed-Solomon codes
remains hard even with unlimited preprocessing, thereby strengthening a result
of Bruck and Naor.Comment: 16 pages, no figure
On the Complexity of Exact Maximum-Likelihood Decoding for Asymptotically Good Low Density Parity Check Codes: A New Perspective
The problem of exact maximum-likelihood (ML) decoding of general linear codes is well-known to be NP-hard. In this paper, we show that exact ML decoding of a class of asymptotically good low density parity check codes â expander codes â over binary symmetric channels (BSCs) is possible with an average-case polynomial complexity. This offers a new way of looking at the complexity issue of exact ML decoding for communication systems where the randomness in channel plays a fundamental central role. More precisely, for any bit-flipping probability p in a nontrivial range, there exists a rate region of non-zero support and a family of asymptotically good codes which achieve error probability exponentially decaying in coding length n while admitting exact ML decoding in average-case polynomial time. As p approaches zero, this rate region approaches the Shannon channel capacity region. Similar results can be extended to AWGN channels, suggesting it may be feasible to eliminate the error floor phenomenon associated with belief-propagation decoding of LDPC codes in the high SNR regime. The derivations are based on a hierarchy of ML certificate decoding algorithms adaptive to the channel realization. In this process, we propose an efficient O(n^2) new ML certificate algorithm based on the max-flow algorithm. Moreover, exact ML decoding of the considered class of codes constructed from LDPC codes with regular left degree, of which the considered expander codes are a special case, remains NP-hard; thus giving an interesting contrast between the worst-case and average-case complexities
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
Achieving a vanishing SNR-gap to exact lattice decoding at a subexponential complexity
The work identifies the first lattice decoding solution that achieves, in the
general outage-limited MIMO setting and in the high-rate and high-SNR limit,
both a vanishing gap to the error-performance of the (DMT optimal) exact
solution of preprocessed lattice decoding, as well as a computational
complexity that is subexponential in the number of codeword bits. The proposed
solution employs lattice reduction (LR)-aided regularized (lattice) sphere
decoding and proper timeout policies. These performance and complexity
guarantees hold for most MIMO scenarios, all reasonable fading statistics, all
channel dimensions and all full-rate lattice codes.
In sharp contrast to the above manageable complexity, the complexity of other
standard preprocessed lattice decoding solutions is shown here to be extremely
high. Specifically the work is first to quantify the complexity of these
lattice (sphere) decoding solutions and to prove the surprising result that the
complexity required to achieve a certain rate-reliability performance, is
exponential in the lattice dimensionality and in the number of codeword bits,
and it in fact matches, in common scenarios, the complexity of ML-based
solutions. Through this sharp contrast, the work was able to, for the first
time, rigorously quantify the pivotal role of lattice reduction as a special
complexity reducing ingredient.
Finally the work analytically refines transceiver DMT analysis which
generally fails to address potentially massive gaps between theory and
practice. Instead the adopted vanishing gap condition guarantees that the
decoder's error curve is arbitrarily close, given a sufficiently high SNR, to
the optimal error curve of exact solutions, which is a much stronger condition
than DMT optimality which only guarantees an error gap that is subpolynomial in
SNR, and can thus be unbounded and generally unacceptable in practical
settings.Comment: 16 pages - submission for IEEE Trans. Inform. Theor
On the Closest Vector Problem with a Distance Guarantee
We present a substantially more efficient variant, both in terms of running
time and size of preprocessing advice, of the algorithm by Liu, Lyubashevsky,
and Micciancio for solving CVPP (the preprocessing version of the Closest
Vector Problem, CVP) with a distance guarantee. For instance, for any , our algorithm finds the (unique) closest lattice point for any target
point whose distance from the lattice is at most times the length of
the shortest nonzero lattice vector, requires as preprocessing advice only vectors, and runs in
time .
As our second main contribution, we present reductions showing that it
suffices to solve CVP, both in its plain and preprocessing versions, when the
input target point is within some bounded distance of the lattice. The
reductions are based on ideas due to Kannan and a recent sparsification
technique due to Dadush and Kun. Combining our reductions with the LLM
algorithm gives an approximation factor of for search
CVPP, improving on the previous best of due to Lagarias, Lenstra,
and Schnorr. When combined with our improved algorithm we obtain, somewhat
surprisingly, that only O(n) vectors of preprocessing advice are sufficient to
solve CVPP with (the only slightly worse) approximation factor of O(n).Comment: An early version of the paper was titled "On Bounded Distance
Decoding and the Closest Vector Problem with Preprocessing". Conference on
Computational Complexity (2014
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