Optimal implicit channel estimation for finite state Markov communication channels
This paper shows the existence of the optimal training, in terms of achievable mutual information rate, for an output feedback implicit estimator for finite-state Markov communication channels. A proper quantification of source redundancy information, implicitly used for channel estimation, is performed. This enables an optimal training rate to be determined as a tradeoff between input signal entropy rate reduction (source redundancy) and channel process entropy rate reduction (channel...[Show more]
|Collections||ANU Research Publications|
|Source:||IEEE International Symposium on Information Theory|
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