14 research outputs found
An Improved LR-aided K-Best Algorithm for MIMO Detection
Recently, lattice reduction (LR) technique has caught great attention for
multi-input multi-output (MIMO) receiver because of its low complexity and high
performance. However, when the number of antennas is large, LR-aided linear
detectors and successive interference cancellation (SIC) detectors still
exhibit considerable performance gap to the optimal maximum likelihood detector
(MLD). To enhance the performance of the LR-aided detectors, the LR-aided
K-best algorithm was developed at the cost of the extra complexity on the order
, where is the number of transmit
antennas and is the number of candidates. In this paper, we develop an
LR-aided K-best algorithm with lower complexity by exploiting a priority queue.
With the aid of the priority queue, our analysis shows that the complexity of
the LR-aided K-best algorithm can be further reduced to . The low complexity of the proposed LR-aided K-best
algorithm allows us to perform the algorithm for large MIMO systems (e.g.,
50x50 MIMO systems) with large candidate sizes. Simulations show that as the
number of antennas increases, the error performance approaches that of AWGN
channel.Comment: 5 pages, 4 figures, 1 table, conferenc
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
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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