Decoding methods: Difference between revisions

Revision as of 18:51, 1 July 2015

Revision as of 16:17, 12 July 2015

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As with ideal observer decoding, a convention must be agreed to for non-unique decoding.

The maximum likelihood decoding problem can also be modeled as an [[integer programming]] problem.<ref name = feldman>{{cite article | title=Using Linear Programming to Decode Binary Linear Codes | first1=Jon | last1=Feldman |first2=Martin J. | last2=Wainwright | first3=David R. | last3=Karger | journal=IEEE Transactions on Information Theory | volume=51 | issue=3 | pages=954–972 | monthdate=March | year=2005 | doi=10.1109/TIT.2004.842696}}</ref>

The maximum likelihood decoding algorithm is an instance of the "marginalize a product function" problem which is solved by applying the [[generalized distributive law]].<ref name=GenDistLaw>{{cite journal | last1=Aji | first1=Srinivas M. | last2=McEliece | first2=Robert J. | title=The Generalized Distributive Law | journal=IEEE Transactions on Information Theory | monthdate=March | year=2000 | volume=46 | issue=2 | pages=325–343 | doi=10.1109/18.825794}}</ref>

==Minimum distance decoding==