4,298 research outputs found
Full Diversity Unitary Precoded Integer-Forcing
We consider a point-to-point flat-fading MIMO channel with channel state
information known both at transmitter and receiver. At the transmitter side, a
lattice coding scheme is employed at each antenna to map information symbols to
independent lattice codewords drawn from the same codebook. Each lattice
codeword is then multiplied by a unitary precoding matrix and sent
through the channel. At the receiver side, an integer-forcing (IF) linear
receiver is employed. We denote this scheme as unitary precoded integer-forcing
(UPIF). We show that UPIF can achieve full-diversity under a constraint based
on the shortest vector of a lattice generated by the precoding matrix . This constraint and a simpler version of that provide design criteria for
two types of full-diversity UPIF. Type I uses a unitary precoder that adapts at
each channel realization. Type II uses a unitary precoder, which remains fixed
for all channel realizations. We then verify our results by computer
simulations in , and MIMO using different QAM
constellations. We finally show that the proposed Type II UPIF outperform the
MIMO precoding X-codes at high data rates.Comment: 12 pages, 8 figures, to appear in IEEE-TW
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
Joint Wyner-Ziv/Dirty Paper coding by modulo-lattice modulation
The combination of source coding with decoder side-information (Wyner-Ziv
problem) and channel coding with encoder side-information (Gel'fand-Pinsker
problem) can be optimally solved using the separation principle. In this work
we show an alternative scheme for the quadratic-Gaussian case, which merges
source and channel coding. This scheme achieves the optimal performance by a
applying modulo-lattice modulation to the analog source. Thus it saves the
complexity of quantization and channel decoding, and remains with the task of
"shaping" only. Furthermore, for high signal-to-noise ratio (SNR), the scheme
approaches the optimal performance using an SNR-independent encoder, thus it is
robust to unknown SNR at the encoder.Comment: Submitted to IEEE Transactions on Information Theory. Presented in
part in ISIT-2006, Seattle. New version after revie
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