851 research outputs found
A Simplified Min-Sum Decoding Algorithm for Non-Binary LDPC Codes
Non-binary low-density parity-check codes are robust to various channel
impairments. However, based on the existing decoding algorithms, the decoder
implementations are expensive because of their excessive computational
complexity and memory usage. Based on the combinatorial optimization, we
present an approximation method for the check node processing. The simulation
results demonstrate that our scheme has small performance loss over the
additive white Gaussian noise channel and independent Rayleigh fading channel.
Furthermore, the proposed reduced-complexity realization provides significant
savings on hardware, so it yields a good performance-complexity tradeoff and
can be efficiently implemented.Comment: Partially presented in ICNC 2012, International Conference on
Computing, Networking and Communications. Accepted by IEEE Transactions on
Communication
Concatenated Turbo/LDPC codes for deep space communications: performance and implementation
Deep space communications require error correction codes able to reach extremely low bit-error-rates, possibly with a steep waterfall region and without error floor. Several schemes have been proposed in the literature to achieve these goals. Most of them rely on the concatenation of different codes that leads to high hardware implementation complexity and poor resource sharing. This work proposes a scheme based on the concatenation of non-custom LDPC and turbo codes that achieves excellent error correction performance. Moreover, since both LDPC and turbo codes can be decoded with the BCJR algorithm, our preliminary results show that an efficient hardware architecture with high resource reuse can be designe
Comparison of Polar Decoders with Existing Low-Density Parity-Check and Turbo Decoders
Polar codes are a recently proposed family of provably capacity-achieving
error-correction codes that received a lot of attention. While their
theoretical properties render them interesting, their practicality compared to
other types of codes has not been thoroughly studied. Towards this end, in this
paper, we perform a comparison of polar decoders against LDPC and Turbo
decoders that are used in existing communications standards. More specifically,
we compare both the error-correction performance and the hardware efficiency of
the corresponding hardware implementations. This comparison enables us to
identify applications where polar codes are superior to existing
error-correction coding solutions as well as to determine the most promising
research direction in terms of the hardware implementation of polar decoders.Comment: Fixes small mistakes from the paper to appear in the proceedings of
IEEE WCNC 2017. Results were presented in the "Polar Coding in Wireless
Communications: Theory and Implementation" Worksho
An Iteratively Decodable Tensor Product Code with Application to Data Storage
The error pattern correcting code (EPCC) can be constructed to provide a
syndrome decoding table targeting the dominant error events of an inter-symbol
interference channel at the output of the Viterbi detector. For the size of the
syndrome table to be manageable and the list of possible error events to be
reasonable in size, the codeword length of EPCC needs to be short enough.
However, the rate of such a short length code will be too low for hard drive
applications. To accommodate the required large redundancy, it is possible to
record only a highly compressed function of the parity bits of EPCC's tensor
product with a symbol correcting code. In this paper, we show that the proposed
tensor error-pattern correcting code (T-EPCC) is linear time encodable and also
devise a low-complexity soft iterative decoding algorithm for EPCC's tensor
product with q-ary LDPC (T-EPCC-qLDPC). Simulation results show that
T-EPCC-qLDPC achieves almost similar performance to single-level qLDPC with a
1/2 KB sector at 50% reduction in decoding complexity. Moreover, 1 KB
T-EPCC-qLDPC surpasses the performance of 1/2 KB single-level qLDPC at the same
decoder complexity.Comment: Hakim Alhussien, Jaekyun Moon, "An Iteratively Decodable Tensor
Product Code with Application to Data Storage
Deriving Good LDPC Convolutional Codes from LDPC Block Codes
Low-density parity-check (LDPC) convolutional codes are capable of achieving
excellent performance with low encoding and decoding complexity. In this paper
we discuss several graph-cover-based methods for deriving families of
time-invariant and time-varying LDPC convolutional codes from LDPC block codes
and show how earlier proposed LDPC convolutional code constructions can be
presented within this framework. Some of the constructed convolutional codes
significantly outperform the underlying LDPC block codes. We investigate some
possible reasons for this "convolutional gain," and we also discuss the ---
mostly moderate --- decoder cost increase that is incurred by going from LDPC
block to LDPC convolutional codes.Comment: Submitted to IEEE Transactions on Information Theory, April 2010;
revised August 2010, revised November 2010 (essentially final version).
(Besides many small changes, the first and second revised versions contain
corrected entries in Tables I and II.
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