662 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 Sampling: A Randomized Lattice Algorithm for Bounded Distance Decoding
Despite its reduced complexity, lattice reduction-aided decoding exhibits a
widening gap to maximum-likelihood (ML) performance as the dimension increases.
To improve its performance, this paper presents randomized lattice decoding
based on Klein's sampling technique, which is a randomized version of Babai's
nearest plane algorithm (i.e., successive interference cancelation (SIC)). To
find the closest lattice point, Klein's algorithm is used to sample some
lattice points and the closest among those samples is chosen. Lattice reduction
increases the probability of finding the closest lattice point, and only needs
to be run once during pre-processing. Further, the sampling can operate very
efficiently in parallel. The technical contribution of this paper is two-fold:
we analyze and optimize the decoding radius of sampling decoding resulting in
better error performance than Klein's original algorithm, and propose a very
efficient implementation of random rounding. Of particular interest is that a
fixed gain in the decoding radius compared to Babai's decoding can be achieved
at polynomial complexity. The proposed decoder is useful for moderate
dimensions where sphere decoding becomes computationally intensive, while
lattice reduction-aided decoding starts to suffer considerable loss. Simulation
results demonstrate near-ML performance is achieved by a moderate number of
samples, even if the dimension is as high as 32
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
DMT Optimality of LR-Aided Linear Decoders for a General Class of Channels, Lattice Designs, and System Models
The work identifies the first general, explicit, and non-random MIMO
encoder-decoder structures that guarantee optimality with respect to the
diversity-multiplexing tradeoff (DMT), without employing a computationally
expensive maximum-likelihood (ML) receiver. Specifically, the work establishes
the DMT optimality of a class of regularized lattice decoders, and more
importantly the DMT optimality of their lattice-reduction (LR)-aided linear
counterparts. The results hold for all channel statistics, for all channel
dimensions, and most interestingly, irrespective of the particular lattice-code
applied. As a special case, it is established that the LLL-based LR-aided
linear implementation of the MMSE-GDFE lattice decoder facilitates DMT optimal
decoding of any lattice code at a worst-case complexity that grows at most
linearly in the data rate. This represents a fundamental reduction in the
decoding complexity when compared to ML decoding whose complexity is generally
exponential in rate.
The results' generality lends them applicable to a plethora of pertinent
communication scenarios such as quasi-static MIMO, MIMO-OFDM, ISI,
cooperative-relaying, and MIMO-ARQ channels, in all of which the DMT optimality
of the LR-aided linear decoder is guaranteed. The adopted approach yields
insight, and motivates further study, into joint transceiver designs with an
improved SNR gap to ML decoding.Comment: 16 pages, 1 figure (3 subfigures), submitted to the IEEE Transactions
on Information Theor
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
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