Guessing noise, not code-words
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ccrcc_isit.pdf
Description
Accepted version
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175.11 KB
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Checksum (MD5)
8d335ef7fe1aa78a2ac707b384f80485
Author(s)
Duffy, Ken R.
Li, Jiange
Medard, Muriel
Date Issued
June 2018
Publisher
IEEE
Citation
Duffy, Ken R., Li, Jiange and Medard, Muriel. 2018. "Guessing noise, not code-words."
Version
Author's final manuscript
Abstract
© 2018 IEEE. We introduce a new algorithm for Maximum Likelihood (ML) decoding for channels with memory. The algorithm is based on the principle that the receiver rank orders noise sequences from most likely to least likely. Subtracting noise from the received signal in that order, the first instance that results in an element of the code-book is the ML decoding. In contrast to traditional approaches, this novel scheme has the desirable property that it becomes more efficient as the code-book rate increases. We establish that the algorithm is capacity achieving for randomly selected code-books. When the code-book rate is less than capacity, we identify asymptotic error exponents as the block length becomes large. When the code-book rate is beyond capacity, we identify asymptotic success exponents. We determine properties of the complexity of the scheme in terms of the number of computations the receiver must perform per block symbol. Worked examples are presented for binary memoryless and Markovian noise. These demonstrate that block-lengths that offer a good complexity-rate tradeoff are typically smaller than the reciprocal of the bit error rate.
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/isit.2018.8437648
