Title :
Concavity of mutual information rate of finite-state channels
Author :
Yonglong Li ; Guangyue Han
Author_Institution :
Univ. of Hong Kong, Hong Kong, China
Abstract :
The computation of the capacity of a finite-state channel (FSC) is a fundamental and long-standing open problem in information theory. The capacity of a memoryless channel can be effectively computed via the classical Blahut-Arimoto algorithm (BAA), which, however, does not apply to a general FSC. Recently Vontobel et al. [1] generalized the BAA to compute the capacity of a finite-state machine channel with a Markovian input. Their proof of the convergence of this algorithm, however, depends on the concavity conjecture posed in their paper. In this paper, we confirm the concavity conjecture for some special FSCs. On the other hand, we give examples to show that the conjecture is not true in general.
Keywords :
Markov processes; channel capacity; convergence of numerical methods; finite state machines; information theory; memoryless systems; Blahut-Arimoto algorithm; Markovian input; algorithm convergence; concavity conjecture; finite state channel; finite state machine channel; information theory; memoryless channel capacity; mutual information rate concavity; Entropy; Hidden Markov models; Markov processes; Mutual information; Power capacitors; Signal to noise ratio;
Conference_Titel :
Information Theory Proceedings (ISIT), 2013 IEEE International Symposium on
Conference_Location :
Istanbul
DOI :
10.1109/ISIT.2013.6620599