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Concavity of the Mutual Information Rate for Input-Restricted Memoryless Channels at High SNR

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2 Author(s)
Guangyue Han ; Department of Mathematics, University of Hong Kong, Hong Kong ; Brian H. Marcus

We consider a memoryless channel with an input Markov process supported on a mixing finite-type constraint. We continue the development of asymptotics for the entropy rate of the output hidden Markov chain and deduce that, at high signal-to-noise ratio, the mutual information rate of such a channel is concave with respect to “almost” all input Markov chains of a given order.

Published in:

IEEE Transactions on Information Theory  (Volume:58 ,  Issue: 3 )