Matrix analysis
Real and complex analysis, 3rd ed.
Real and complex analysis, 3rd ed.
Linear stochastic systems
Minimum entropy H∞ control
Determinant Maximization with Linear Matrix Inequality Constraints
SIAM Journal on Matrix Analysis and Applications
Convex analysis and variational problems
Convex analysis and variational problems
Optimization by Vector Space Methods
Optimization by Vector Space Methods
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Feedback capacity of finite-state machine channels
IEEE Transactions on Information Theory
Feedback capacity of the first-order moving average Gaussian channel
IEEE Transactions on Information Theory
On the Feedback Capacity of Power-Constrained Gaussian Noise Channels With Memory
IEEE Transactions on Information Theory
A Coding Theorem for a Class of Stationary Channels With Feedback
IEEE Transactions on Information Theory
Directed information and causal estimation in continuous time
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
Zero-rate feedback can achieve the empirical capacity
IEEE Transactions on Information Theory
A little feedback can simplify sensor network cooperation
IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
Wideband fading channels with feedback
IEEE Transactions on Information Theory
Bode-like integral for stochastic switched systems in the presence of limited information
Automatica (Journal of IFAC)
Hi-index | 754.96 |
The feedback capacity of additive stationary Gaussian noise channels is characterized as the solution to a variational problem in the noise power spectral density. When specialized to the first-order autoregressive moving-average noise spectrum, this variational characterization yields a closed-form expression for the feedback capacity. In particular, this result shows that the celebrated Schalkwijk-Kailath coding achieves the feedback capacity for the first-order autoregressive moving-average Gaussian channel, positively answering a long-standing open problem studied by Butman, Tiernan-Schalkwijk, Wolfowitz, Ozarow, Ordentlich, Yang-Kavčic-Tatikonda, and others. More generally, it is shown that a k-dimensional generalization of the Schalkwijk-Kailath coding achieves the feedback capacity for any autoregressive moving-average noise spectrum of order k. Simply put, the optimal transmitter iteratively refines the receiver's knowledge of the intended message. This development reveals intriguing connections between estimation, control, and feedback communication.