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Over binary input channels, the uniform distribution is a universal prior, in the sense that it maximizes the worst case mutual information of all binary input channels and achieves at least 94.2% of the capacity. In this paper, we address a similar question. We look for the best collection of finitely many a posteriori metrics, to maximize the worst case mismatched mutual information achieved by...
On a discrete memoryless channel (DMC), the maximum likelihood (ML) decoding rule is not only optimal (for equiprobable messages and average error probability), but it is also linear (the metric that it maximizes is additive over the block length), which in particular, makes ML practically conceivable. On a compound DMC, the use of ML is ruled out by the channelpsilas law ignorance. In order to account...
We study communication over an unknown, possibly unreliable, discrete memoryless channel. For such problems, an erasure option at the decoder is desirable. We use constant-composition random codes and propose a generalization of the Maximum Mutual Information decoder. The proposed decoder is parameterized by a weighting function that can be designed to optimize the fundamental tradeoff between undetected-error...
Multilevel coding (MLC) is a method for constructing error correcting codes from codes with smaller alphabets. Using maximum likelihood decoding (MLD) or multistage decoding (MSD) they can be used for communication over channels with larger alphabets. It is widely known that MLC with MSD may achieve the channel capacity for many channels. Such results are based the chain rule of the mutual information...
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