3,737 research outputs found
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
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Code diversity in multiple antenna wireless communication
The standard approach to the design of individual space-time codes is based
on optimizing diversity and coding gains. This geometric approach leads to
remarkable examples, such as perfect space-time block codes, for which the
complexity of Maximum Likelihood (ML) decoding is considerable. Code diversity
is an alternative and complementary approach where a small number of feedback
bits are used to select from a family of space-time codes. Different codes lead
to different induced channels at the receiver, where Channel State Information
(CSI) is used to instruct the transmitter how to choose the code. This method
of feedback provides gains associated with beamforming while minimizing the
number of feedback bits. It complements the standard approach to code design by
taking advantage of different (possibly equivalent) realizations of a
particular code design. Feedback can be combined with sub-optimal low
complexity decoding of the component codes to match ML decoding performance of
any individual code in the family. It can also be combined with ML decoding of
the component codes to improve performance beyond ML decoding performance of
any individual code. One method of implementing code diversity is the use of
feedback to adapt the phase of a transmitted signal as shown for 4 by 4
Quasi-Orthogonal Space-Time Block Code (QOSTBC) and multi-user detection using
the Alamouti code. Code diversity implemented by selecting from equivalent
variants is used to improve ML decoding performance of the Golden code. This
paper introduces a family of full rate circulant codes which can be linearly
decoded by fourier decomposition of circulant matrices within the code
diversity framework. A 3 by 3 circulant code is shown to outperform the
Alamouti code at the same transmission rate.Comment: 9 page
Achieving Low-Complexity Maximum-Likelihood Detection for the 3D MIMO Code
The 3D MIMO code is a robust and efficient space-time block code (STBC) for
the distributed MIMO broadcasting but suffers from high maximum-likelihood (ML)
decoding complexity. In this paper, we first analyze some properties of the 3D
MIMO code to show that the 3D MIMO code is fast-decodable. It is proved that
the ML decoding performance can be achieved with a complexity of O(M^{4.5})
instead of O(M^8) in quasi static channel with M-ary square QAM modulations.
Consequently, we propose a simplified ML decoder exploiting the unique
properties of 3D MIMO code. Simulation results show that the proposed
simplified ML decoder can achieve much lower processing time latency compared
to the classical sphere decoder with Schnorr-Euchner enumeration
A New Low-Complexity Decodable Rate-5/4 STBC for Four Transmit Antennas with Nonvanishing Determinants
The use of Space-Time Block Codes (STBCs) increases significantly the optimal
detection complexity at the receiver unless the low-complexity decodability
property is taken into consideration in the STBC design. In this paper we
propose a new low-complexity decodable rate-5/4 full-diversity 4 x 4 STBC. We
provide an analytical proof that the proposed code has the
Non-Vanishing-Determinant (NVD) property, a property that can be exploited
through the use of adaptive modulation which changes the transmission rate
according to the wireless channel quality. We compare the proposed code to the
best existing low-complexity decodable rate-5/4 full-diversity 4 x 4 STBC in
terms of performance over quasi-static Rayleigh fading channels, worst- case
complexity, average complexity, and Peak-to-Average Power Ratio (PAPR). Our
code is found to provide better performance, lower average decoding complexity,
and lower PAPR at the expense of a slight increase in worst-case decoding
complexity.Comment: 5 pages, 2 figures and 1 table; IEEE Global Telecommunications
Conference (GLOBECOM 2011), 201
Enhanced Feedback Iterative Decoding of Sparse Quantum Codes
Decoding sparse quantum codes can be accomplished by syndrome-based decoding
using a belief propagation (BP) algorithm.We significantly improve this
decoding scheme by developing a new feedback adjustment strategy for the
standard BP algorithm. In our feedback procedure, we exploit much of the
information from stabilizers, not just the syndrome but also the values of the
frustrated checks on individual qubits of the code and the channel model.
Furthermore we show that our decoding algorithm is superior to belief
propagation algorithms using only the syndrome in the feedback procedure for
all cases of the depolarizing channel. Our algorithm does not increase the
measurement overhead compared to the previous method, as the extra information
comes for free from the requisite stabilizer measurements.Comment: 10 pages, 11 figures, Second version, To be appeared in IEEE
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