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dc.contributor.authorCohen, Alejandro
dc.contributor.authorSolomon, Amit
dc.contributor.authorDuffy, Ken R.
dc.contributor.authorMédard, Muriel
dc.date.accessioned2021-11-08T16:26:38Z
dc.date.available2021-11-08T14:40:39Z
dc.date.available2021-11-08T16:26:38Z
dc.date.issued2020-06
dc.identifier.urihttps://hdl.handle.net/1721.1/137668.2
dc.description.abstract© 2020 IEEE. We introduce Noise Recycling, a method that enhances decoding performance of channels subject to correlated noise without joint decoding. The method can be used with any combination of codes, code-rates and decoding techniques. In the approach, a continuous realization of noise is estimated from a lead channel by subtracting its decoded output from its received signal. This estimate is then used to improve the accuracy of decoding of an orthogonal channel that is experiencing correlated noise. In this design, channels aid each other only through the provision of noise estimates post-decoding. In a Gauss-Markov model of correlated noise, we constructively establish that noise recycling employing a simple successive order enables higher rates than not recycling noise. Simulations illustrate noise recycling can be employed with any code and decoder, and that noise recycling shows Block Error Rate (BLER) benefits when applying the same predetermined order as used to enhance the rate region. Finally, for short codes we establish that an additional BLER improvement is possible through noise recycling with racing, where the lead channel is not pre-determined, but is chosen on the fly based on which decoder completes first.en_US
dc.language.isoen
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ISIT44484.2020.9174406en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleNoise Recyclingen_US
dc.typeArticleen_US
dc.identifier.citation2020. "Noise Recycling." IEEE International Symposium on Information Theory - Proceedings, 2020-June.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Research Laboratory of Electronicsen_US
dc.relation.journalIEEE International Symposium on Information Theory - Proceedingsen_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2021-03-09T18:15:23Z
dspace.orderedauthorsCohen, A; Solomon, A; Duffy, KR; Medard, Men_US
dspace.date.submission2021-03-09T18:15:24Z
mit.journal.volume2020-Juneen_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusPublication Information Neededen_US


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