615 research outputs found
Efficient LLR Calculation for Non-Binary Modulations over Fading Channels
Log-likelihood ratio (LLR) computation for non-binary modulations over fading
channels is complicated. A measure of LLR accuracy on asymmetric binary
channels is introduced to facilitate good LLR approximations for non-binary
modulations. Considering piecewise linear LLR approximations, we prove
convexity of optimizing the coefficients according to this measure. For the
optimized approximate LLRs, we report negligible performance losses compared to
true LLRs.Comment: Submitted to IEEE Transactions on Communication
Turbo EP-based Equalization: a Filter-Type Implementation
This manuscript has been submitted to Transactions on Communications on
September 7, 2017; revised on January 10, 2018 and March 27, 2018; and accepted
on April 25, 2018
We propose a novel filter-type equalizer to improve the solution of the
linear minimum-mean squared-error (LMMSE) turbo equalizer, with computational
complexity constrained to be quadratic in the filter length. When high-order
modulations and/or large memory channels are used the optimal BCJR equalizer is
unavailable, due to its computational complexity. In this scenario, the
filter-type LMMSE turbo equalization exhibits a good performance compared to
other approximations. In this paper, we show that this solution can be
significantly improved by using expectation propagation (EP) in the estimation
of the a posteriori probabilities. First, it yields a more accurate estimation
of the extrinsic distribution to be sent to the channel decoder. Second,
compared to other solutions based on EP the computational complexity of the
proposed solution is constrained to be quadratic in the length of the finite
impulse response (FIR). In addition, we review previous EP-based turbo
equalization implementations. Instead of considering default uniform priors we
exploit the outputs of the decoder. Some simulation results are included to
show that this new EP-based filter remarkably outperforms the turbo approach of
previous versions of the EP algorithm and also improves the LMMSE solution,
with and without turbo equalization
Optimized Bit Mappings for Spatially Coupled LDPC Codes over Parallel Binary Erasure Channels
In many practical communication systems, one binary encoder/decoder pair is
used to communicate over a set of parallel channels. Examples of this setup
include multi-carrier transmission, rate-compatible puncturing of turbo-like
codes, and bit-interleaved coded modulation (BICM). A bit mapper is commonly
employed to determine how the coded bits are allocated to the channels. In this
paper, we study spatially coupled low-density parity check codes over parallel
channels and optimize the bit mapper using BICM as the driving example. For
simplicity, the parallel bit channels that arise in BICM are replaced by
independent binary erasure channels (BECs). For two parallel BECs modeled
according to a 4-PAM constellation labeled by the binary reflected Gray code,
the optimization results show that the decoding threshold can be improved over
a uniform random bit mapper, or, alternatively, the spatial chain length of the
code can be reduced for a given gap to capacity. It is also shown that for
rate-loss free, circular (tail-biting) ensembles, a decoding wave effect can be
initiated using only an optimized bit mapper
A new approach to optimise Non-Binary LDPC codes for Coded Modulations
International audienceThis paper is dedicated to the optimisation of Non-Binary LDPC codes when associated to high-order modulations. To be specific, we propose to specify the values of the non-zero NB-LDPC parity matrix coefficients depending on the corresponding check node equation and the Euclidean distance of the coded modulation. In other words, we explore the joint optimisation of the modulation mapping and the non-binary matrix. The performance gains announced by a theoretical analysis based on the Union Bound are confirmed by simulations results. We obtain an 0.2-dB gain in the high SNR regime compared to other state-of-the-art matrices
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