18,598 research outputs found
A Decoding Algorithm for LDPC Codes Over Erasure Channels with Sporadic Errors
none4An efficient decoding algorithm for low-density parity-check (LDPC) codes on erasure channels with sporadic errors (i.e., binary error-and-erasure channels with error probability much smaller than the erasure probability) is proposed and its performance analyzed. A general single-error multiple-erasure (SEME) decoding algorithm is first described, which may be in principle used with any binary linear block code. The algorithm is optimum whenever the non-erased part of the received word is affected by at most one error, and is capable of performing error detection of multiple errors. An upper bound on the average block error probability under SEME decoding is derived for the linear random code ensemble. The bound is tight and easy to implement. The algorithm is then adapted to LDPC codes, resulting in a simple modification to a previously proposed efficient maximum likelihood LDPC erasure decoder which exploits the parity-check matrix sparseness. Numerical results reveal that LDPC codes under efficient SEME decoding can closely approach the average performance of random codes.noneG. Liva; E. Paolini; B. Matuz; M. ChianiG. Liva; E. Paolini; B. Matuz; M. Chian
From Polar to Reed-Muller Codes: a Technique to Improve the Finite-Length Performance
We explore the relationship between polar and RM codes and we describe a
coding scheme which improves upon the performance of the standard polar code at
practical block lengths. Our starting point is the experimental observation
that RM codes have a smaller error probability than polar codes under MAP
decoding. This motivates us to introduce a family of codes that "interpolates"
between RM and polar codes, call this family , where is
the original polar code, and is an RM code.
Based on numerical observations, we remark that the error probability under MAP
decoding is an increasing function of . MAP decoding has in general
exponential complexity, but empirically the performance of polar codes at
finite block lengths is boosted by moving along the family even under low-complexity decoding schemes such as, for instance,
belief propagation or successive cancellation list decoder. We demonstrate the
performance gain via numerical simulations for transmission over the erasure
channel as well as the Gaussian channel.Comment: 8 pages, 7 figures, in IEEE Transactions on Communications, 2014 and
in ISIT'1
Minimum-Variance Importance-Sampling Bernoulli Estimator for Fast Simulation of Linear Block Codes over Binary Symmetric Channels
In this paper the choice of the Bernoulli distribution as biased distribution
for importance sampling (IS) Monte-Carlo (MC) simulation of linear block codes
over binary symmetric channels (BSCs) is studied. Based on the analytical
derivation of the optimal IS Bernoulli distribution, with explicit calculation
of the variance of the corresponding IS estimator, two novel algorithms for
fast-simulation of linear block codes are proposed. For sufficiently high
signal-to-noise ratios (SNRs) one of the proposed algorithm is SNR-invariant,
i.e. the IS estimator does not depend on the cross-over probability of the
channel. Also, the proposed algorithms are shown to be suitable for the
estimation of the error-correcting capability of the code and the decoder.
Finally, the effectiveness of the algorithms is confirmed through simulation
results in comparison to standard Monte Carlo method
Coding with Encoding Uncertainty
We study the channel coding problem when errors and uncertainty occur in the
encoding process. For simplicity we assume the channel between the encoder and
the decoder is perfect. Focusing on linear block codes, we model the encoding
uncertainty as erasures on the edges in the factor graph of the encoder
generator matrix. We first take a worst-case approach and find the maximum
tolerable number of erasures for perfect error correction. Next, we take a
probabilistic approach and derive a sufficient condition on the rate of a set
of codes, such that decoding error probability vanishes as blocklength tends to
infinity. In both scenarios, due to the inherent asymmetry of the problem, we
derive the results from first principles, which indicates that robustness to
encoding errors requires new properties of codes different from classical
properties.Comment: 12 pages; a shorter version of this work will appear in the
proceedings of ISIT 201
The Error Probability of Sparse Superposition Codes with Approximate Message Passing Decoding
Sparse superposition codes, or sparse regression codes (SPARCs), are a recent class of codes for reliable communication over the AWGN channel at rates approaching the channel capacity. Approximate
message passing (AMP) decoding, a computationally efficient technique for decoding SPARCs, has been proven to be asymptotically capacity-achieving for the AWGN channel. In this paper, we refine the asymptotic result by deriving a large deviations bound on the probability of AMP decoding error. This bound gives insight into the error performance of the AMP decoder for large but finite problem sizes, giving an error exponent as well as guidance on how the code parameters should be chosen at finite block lengths. For an appropriate choice of code parameters, we show that for any fixed rate less than the channel capacity, the decoding error probability decays exponentially in , where , the number of AMP iterations required for successful decoding, is bounded in terms of the gap from capacity
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