Sequential Necessary and Sufficient Conditions for Capacity Achieving Distributions of Channels with Memory and Feedback (1604.02742v2)

Abstract: We derive sequential necessary and sufficient conditions for any channel input conditional distribution ${\cal P}{0,n}\triangleq{P{X_t|X^{t-1},Y^{t-1}}:~t=0,\ldots,n}$ to maximize the finite-time horizon directed information defined by $$C^{FB}_{X^n \rightarrow Y^n} \triangleq \sup_{{\cal P}{0,n}} I(X^n\rightarrow{Y^n}),~~~ I(X^n \rightarrow Y^n) =\sum{t=0}^n{I}(X^t;Y_t|Y^{t-1})$$ for channel distributions ${P_{Y_t|Y^{t-1},X_t}:~t=0,\ldots,n}$ and ${P_{Y_t|Y_{t-M}^{t-1},X_t}:~t=0,\ldots,n}$, where $Y^t\triangleq{Y_0,\ldots,Y_t}$ and $X^t\triangleq{X_0,\ldots,X_t}$ are the channel input and output random processes, and $M$ is a finite nonnegative integer. \noi We apply the necessary and sufficient conditions to application examples of time-varying channels with memory and we derive recursive closed form expressions of the optimal distributions, which maximize the finite-time horizon directed information. Further, we derive the feedback capacity from the asymptotic properties of the optimal distributions by investigating the limit $$C_{X^\infty \rightarrow Y^\infty}^{FB} \triangleq \lim_{n \longrightarrow \infty} \frac{1}{n+1} C_{X^n \rightarrow Y^n}^{FB}$$ without any \'a priori assumptions, such as, stationarity, ergodicity or irreducibility of the channel distribution. The necessary and sufficient conditions can be easily extended to a variety of channels with memory, beyond the ones considered in this paper.