256 research outputs found
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
Space-time coding techniques with bit-interleaved coded modulations for MIMO block-fading channels
The space-time bit-interleaved coded modulation (ST-BICM) is an efficient
technique to obtain high diversity and coding gain on a block-fading MIMO
channel. Its maximum-likelihood (ML) performance is computed under ideal
interleaving conditions, which enables a global optimization taking into
account channel coding. Thanks to a diversity upperbound derived from the
Singleton bound, an appropriate choice of the time dimension of the space-time
coding is possible, which maximizes diversity while minimizing complexity.
Based on the analysis, an optimized interleaver and a set of linear precoders,
called dispersive nucleo algebraic (DNA) precoders are proposed. The proposed
precoders have good performance with respect to the state of the art and exist
for any number of transmit antennas and any time dimension. With turbo codes,
they exhibit a frame error rate which does not increase with frame length.Comment: Submitted to IEEE Trans. on Information Theory, Submission: January
2006 - First review: June 200
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
Asymptotic Performance of Linear Receivers in MIMO Fading Channels
Linear receivers are an attractive low-complexity alternative to optimal
processing for multi-antenna MIMO communications. In this paper we characterize
the information-theoretic performance of MIMO linear receivers in two different
asymptotic regimes. For fixed number of antennas, we investigate the limit of
error probability in the high-SNR regime in terms of the Diversity-Multiplexing
Tradeoff (DMT). Following this, we characterize the error probability for fixed
SNR in the regime of large (but finite) number of antennas.
As far as the DMT is concerned, we report a negative result: we show that
both linear Zero-Forcing (ZF) and linear Minimum Mean-Square Error (MMSE)
receivers achieve the same DMT, which is largely suboptimal even in the case
where outer coding and decoding is performed across the antennas. We also
provide an approximate quantitative analysis of the markedly different behavior
of the MMSE and ZF receivers at finite rate and non-asymptotic SNR, and show
that while the ZF receiver achieves poor diversity at any finite rate, the MMSE
receiver error curve slope flattens out progressively, as the coding rate
increases.
When SNR is fixed and the number of antennas becomes large, we show that the
mutual information at the output of a MMSE or ZF linear receiver has
fluctuations that converge in distribution to a Gaussian random variable, whose
mean and variance can be characterized in closed form. This analysis extends to
the linear receiver case a well-known result previously obtained for the
optimal receiver. Simulations reveal that the asymptotic analysis captures
accurately the outage behavior of systems even with a moderate number of
antennas.Comment: 48 pages, Submitted to IEEE Transactions on Information Theor
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