Capacity-Achieving Guessing Random Additive Noise Decoding

We introduce a new algorithm for realizing maximum likelihood (ML) decoding for arbitrary codebooks in discrete channels with or without memory, in which the receiver rank-orders noise sequences from most likely to least likely. Subtracting noise from the received signal in that order, the first ins...

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Bibliographic Details
Published in:	IEEE transactions on information theory Vol. 65; no. 7; pp. 4023 - 4040
Main Authors:	Duffy, Ken R., Li, Jiange, Medard, Muriel
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.07.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Abandonment Algorithms approximate ML decoding Approximation algorithms Channel coding Complexity Complexity theory Decoding Discrete channels error probability Exponents Manganese Markov processes Maximum likelihood decoding Noise Receivers
ISSN:	0018-9448, 1557-9654
Online Access:	Get full text
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