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Finite State Channels with Time-Invariant Deterministic Feedback

Abstract

We consider capacity of discrete-time channels with feedback for the general case where the feedback is a time-invariant deterministic function of the output samples. Under the assumption that the channel states take values in a finite alphabet, we find a sequence of achievable rates and a sequence of upper bounds on the capacity. The achievable rates and the upper bounds are computable for any $N$, and the limits of the sequences exist. We show that when the probability of the initial state is positive for all the channel states, then the capacity is the limit of the achievable-rate sequence. We further show that when the channel is stationary, indecomposable, and has no intersymbol interference (ISI), its capacity is given by the limit of the maximum of the (normalized) directed information between the input $X^{N}$ and the output $Y^{N}$, i.e., $$C = \lim _{N \rightarrow \infty } {{1}\over {N}} \max I(X^{N} \rightarrow Y^{N})$$ where the maximization is taken over the causal conditioning probability $Q(x^{N}\Vert z^{N-1})$ defined in this paper. The main idea for obtaining the results is to add causality into Gallager's results on finite state channels. The capacity results are used to show that the source–channel separation theorem holds for time-invariant determinist feedback, and if the state of the channel is known both at the encoder and the decoder, then feedback does not increase capacity.


Publication:
IEEE Transactions on Information Theory
Pub Date:
2009
DOI:

10.1109/TIT.2008.2009849

Bibcode:
2009ITIT...55..644P
Keywords:
  • Causal conditioning;
  • code-tree;
  • directed information;
  • feedback capacity;
  • maximum likelihood;
  • random coding;
  • source–channel coding separation
full text sources
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