Statistical approach to ML decoding of linear block codes on symmetric channels
- Creators
- Vikalo, Haris
- Hassibi, Babak
Abstract
Maximum-likelihood (ML) decoding of linear block codes on a symmetric channel is studied. Exact ML decoding is known to be computationally difficult. We propose an algorithm that finds the exact solution to the ML decoding problem by performing a depth-first search on a tree. The tree is designed from the code generator matrix and pruned based on the statistics of the channel noise. The complexity of the algorithm is a random variable. We characterize the complexity by means of its first moment, which for binary symmetric channels we find in closed-form. The obtained results indicate that the expected complexity of the algorithm is low over a wide range of system parameters.
Additional Information
© 2004 IEEE. This work was supported in part by the National Science Foundation under grant no. CCR-0133818, by the Office of Naval Research under grant no. N00014-02-1-0578, and by Caltech's Lee Center for Advanced Networking.Attached Files
Published - Statistical_approach_to_ML_decoding_of_linear_block_codes_on_symmetric_channels.pdf
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Additional details
- Eprint ID
- 54626
- Resolver ID
- CaltechAUTHORS:20150210-073800640
- NSF
- CCR-0133818
- Office of Naval Research (ONR)
- N00014-02-1-0578
- Caltech Lee Center for Advanced Networking
- Created
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2015-02-11Created from EPrint's datestamp field
- Updated
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2021-11-10Created from EPrint's last_modified field