Title : 
Feedback Capacity of a Class of Symmetric Finite-State Markov Channels
         
        
            Author : 
Nevroz Sen;Fady Alajaji;Serdar Yuksel
         
        
            Author_Institution : 
Department of Mathematics and Statistics, Queen´s University, Kingston, Canada
         
        
        
        
        
        
        
            Abstract : 
We consider the feedback capacity of a class of symmetric finite-state Markov channels. Here, symmetry (termed “quasi-symmetry”) is defined as a generalized version of the symmetry defined for discrete memoryless channels. The symmetry yields the existence of a hidden Markov noise process that depends on the channel´s state process and facilitates the channel description as a function of input and noise, where the function satisfies a desirable invertibility property. We show that feedback does not increase capacity for such class of finite-state channels and that both their nonfeedback and feedback capacities are achieved by an independent and uniformly distributed (i.u.d.) input. As a result, the channel capacity is explicitly given as a difference of output and noise entropy rates, where the output is driven by the i.u.d. input.
         
        
            Keywords : 
"Markov processes","Noise","Entropy","Symmetric matrices","Channel capacity","Hidden Markov models","Dynamic programming"
         
        
            Journal_Title : 
IEEE Transactions on Information Theory
         
        
        
        
        
            DOI : 
10.1109/TIT.2011.2146350