Feedback Capacity of MIMO Gaussian Channels

Finding a computable expression for the feedback capacity of channels with non-white Gaussian, additive noise is a long standing open problem. In this paper, we solve this problem in the scenario where the channel has multiple inputs and multiple outputs (MIMO) and the noise process is generated as the output of a state-space model (a hidden Markov model). The main result is a computable characterization of the feedback capacity as a finite-dimensional convex optimization problem. Our solution subsumes all previous solutions to the feedback capacity including the auto-regressive moving-average (ARMA) noise process of first order, even if it is a non-stationary process. The capacity problem can be viewed as the problem of maximizing the measurements' entropy rate of a controlled (policy-dependent) state-space subject to a power constraint. We formulate the finite-block version of this problem as a sequential convex optimization problem, which in turn leads to a single-letter and computable upper bound. By optimizing over a family of time-invariant policies that correspond to the channel inputs distribution, a tight lower bound is realized. We show that one of the optimization constraints in the capacity characterization boils down to a Riccati equation, revealing an interesting relation between explicit capacity formulae and Riccati equations.

Automated summary

In this paper, the computable expression for the feedback capacity of channels with non-white Gaussian noise is solved in the scenario of MIMO channels and a state-space noise model. The solution involves a finite-dimensional convex optimization problem and unveils the relation between the capacity formulae and Riccati equations. The problem is viewed as maximizing the measurements' entropy rate subject to a power constraint, leading to a tight lower bound by optimizing over a family of time-invariant policies.