3,144 research outputs found
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
Belief Propagation Decoding of Polar Codes on Permuted Factor Graphs
We show that the performance of iterative belief propagation (BP) decoding of
polar codes can be enhanced by decoding over different carefully chosen factor
graph realizations. With a genie-aided stopping condition, it can achieve the
successive cancellation list (SCL) decoding performance which has already been
shown to achieve the maximum likelihood (ML) bound provided that the list size
is sufficiently large. The proposed decoder is based on different realizations
of the polar code factor graph with randomly permuted stages during decoding.
Additionally, a different way of visualizing the polar code factor graph is
presented, facilitating the analysis of the underlying factor graph and the
comparison of different graph permutations. In our proposed decoder, a high
rate Cyclic Redundancy Check (CRC) code is concatenated with a polar code and
used as an iteration stopping criterion (i.e., genie) to even outperform the
SCL decoder of the plain polar code (without the CRC-aid). Although our
permuted factor graph-based decoder does not outperform the SCL-CRC decoder, it
achieves, to the best of our knowledge, the best performance of all iterative
polar decoders presented thus far.Comment: in IEEE Wireless Commun. and Networking Conf. (WCNC), April 201
On joint detection and decoding of linear block codes on Gaussian vector channels
Optimal receivers recovering signals transmitted across noisy communication channels employ a maximum-likelihood (ML) criterion to minimize the probability of error. The problem of finding the most likely transmitted symbol is often equivalent to finding the closest lattice point to a given point and is known to be NP-hard. In systems that employ error-correcting coding for data protection, the symbol space forms a sparse lattice, where the sparsity structure is determined by the code. In such systems, ML data recovery may be geometrically interpreted as a search for the closest point in the sparse lattice. In this paper, motivated by the idea of the "sphere decoding" algorithm of Fincke and Pohst, we propose an algorithm that finds the closest point in the sparse lattice to the given vector. This given vector is not arbitrary, but rather is an unknown sparse lattice point that has been perturbed by an additive noise vector whose statistical properties are known. The complexity of the proposed algorithm is thus a random variable. We study its expected value, averaged over the noise and over the lattice. For binary linear block codes, we find the expected complexity in closed form. Simulation results indicate significant performance gains over systems employing separate detection and decoding, yet are obtained at a complexity that is practically feasible over a wide range of system parameters
Iterative Soft Input Soft Output Decoding of Reed-Solomon Codes by Adapting the Parity Check Matrix
An iterative algorithm is presented for soft-input-soft-output (SISO)
decoding of Reed-Solomon (RS) codes. The proposed iterative algorithm uses the
sum product algorithm (SPA) in conjunction with a binary parity check matrix of
the RS code. The novelty is in reducing a submatrix of the binary parity check
matrix that corresponds to less reliable bits to a sparse nature before the SPA
is applied at each iteration. The proposed algorithm can be geometrically
interpreted as a two-stage gradient descent with an adaptive potential
function. This adaptive procedure is crucial to the convergence behavior of the
gradient descent algorithm and, therefore, significantly improves the
performance. Simulation results show that the proposed decoding algorithm and
its variations provide significant gain over hard decision decoding (HDD) and
compare favorably with other popular soft decision decoding methods.Comment: 10 pages, 10 figures, final version accepted by IEEE Trans. on
Information Theor
A Reduced Latency List Decoding Algorithm for Polar Codes
Long polar codes can achieve the capacity of arbitrary binary-input discrete
memoryless channels under a low complexity successive cancelation (SC) decoding
algorithm. But for polar codes with short and moderate code length, the
decoding performance of the SC decoding algorithm is inferior. The cyclic
redundancy check (CRC) aided successive cancelation list (SCL) decoding
algorithm has better error performance than the SC decoding algorithm for short
or moderate polar codes. However, the CRC aided SCL (CA-SCL) decoding algorithm
still suffer from long decoding latency. In this paper, a reduced latency list
decoding (RLLD) algorithm for polar codes is proposed. For the proposed RLLD
algorithm, all rate-0 nodes and part of rate-1 nodes are decoded instantly
without traversing the corresponding subtree. A list maximum-likelihood
decoding (LMLD) algorithm is proposed to decode the maximum likelihood (ML)
nodes and the remaining rate-1 nodes. Moreover, a simplified LMLD (SLMLD)
algorithm is also proposed to reduce the computational complexity of the LMLD
algorithm. Suppose a partial parallel list decoder architecture with list size
is used, for an (8192, 4096) polar code, the proposed RLLD algorithm can
reduce the number of decoding clock cycles and decoding latency by 6.97 and
6.77 times, respectively.Comment: 7 pages, accepted by 2014 IEEE International Workshop on Signal
Processing Systems (SiPS
The Error-Pattern-Correcting Turbo Equalizer
The error-pattern correcting code (EPCC) is incorporated in the design of a
turbo equalizer (TE) with aim to correct dominant error events of the
inter-symbol interference (ISI) channel at the output of its matching Viterbi
detector. By targeting the low Hamming-weight interleaved errors of the outer
convolutional code, which are responsible for low Euclidean-weight errors in
the Viterbi trellis, the turbo equalizer with an error-pattern correcting code
(TE-EPCC) exhibits a much lower bit-error rate (BER) floor compared to the
conventional non-precoded TE, especially for high rate applications. A
maximum-likelihood upper bound is developed on the BER floor of the TE-EPCC for
a generalized two-tap ISI channel, in order to study TE-EPCC's signal-to-noise
ratio (SNR) gain for various channel conditions and design parameters. In
addition, the SNR gain of the TE-EPCC relative to an existing precoded TE is
compared to demonstrate the present TE's superiority for short interleaver
lengths and high coding rates.Comment: This work has been submitted to the special issue of the IEEE
Transactions on Information Theory titled: "Facets of Coding Theory: from
Algorithms to Networks". This work was supported in part by the NSF
Theoretical Foundation Grant 0728676
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