6,445 research outputs found
Order Statistics Based List Decoding Techniques for Linear Binary Block Codes
The order statistics based list decoding techniques for linear binary block
codes of small to medium block length are investigated. The construction of the
list of the test error patterns is considered. The original order statistics
decoding is generalized by assuming segmentation of the most reliable
independent positions of the received bits. The segmentation is shown to
overcome several drawbacks of the original order statistics decoding. The
complexity of the order statistics based decoding is further reduced by
assuming a partial ordering of the received bits in order to avoid the complex
Gauss elimination. The probability of the test error patterns in the decoding
list is derived. The bit error rate performance and the decoding complexity
trade-off of the proposed decoding algorithms is studied by computer
simulations. Numerical examples show that, in some cases, the proposed decoding
schemes are superior to the original order statistics decoding in terms of both
the bit error rate performance as well as the decoding complexity.Comment: 17 pages, 2 tables, 6 figures, submitted to IEEE Transactions on
Information Theor
Near maximum likelihood soft-decision decoding of a particular class of rate-1/2 systematic linear block codes
International audiencePresented is a soft-decision decoding algorithm for a particular class of rate-1/2 systematic linear block codes. The proposed algorithm performs successive re-encoding of both the data and parity bits, to produce a list of codewords among which the most likely candidate is chosen. Simulation results show that close to optimal performance can be obtained at reasonable complexity. They validate the potential of the proposed algorithm as a practical approach for soft-decision decoding
Ordered Reliability Direct Error Pattern Testing Decoding Algorithm
We introduce a novel universal soft-decision decoding algorithm for binary
block codes called ordered reliability direct error pattern testing (ORDEPT).
Our results, obtained for a variety of popular short high-rate codes,
demonstrate that ORDEPT outperforms state-of-the-art decoding algorithms of
comparable complexity such as ordered reliability bits guessing random additive
noise decoding (ORBGRAND) in terms of the decoding error probability and
latency. The improvements carry on to the iterative decoding of product codes
and convolutional product-like codes, where we present a new adaptive decoding
algorithm and demonstrate the ability of ORDEPT to efficiently find multiple
candidate codewords to produce soft output
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