A Caltech Library Service

Feedback Capacity of Gaussian Channels with Memory

Sabag, Oron and Kostina, Victoria and Hassibi, Babak (2022) Feedback Capacity of Gaussian Channels with Memory. In: 2022 IEEE International Symposium on Information Theory (ISIT). IEEE , Piscataway, NJ, pp. 2547-2552. ISBN 978-1-6654-2159-1.

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item:


We consider the feedback capacity of a MIMO channel whose channel output is given by a linear state-space model driven by the channel inputs and a Gaussian process. The generality of our state-space model subsumes all previous studied models such as additive channels with colored Gaussian noise, and channels with an arbitrary dependence on previous channel inputs or outputs. The main result is a computable feedback capacity expression that is given as a convex optimization problem subject to a detectability condition. We demonstrate the capacity result on the auto-regressive Gaussian noise channel, where we show that even a single time-instance delay in the feedback reduces the feedback capacity significantly in the stationary regime. On the other hand, for large regression parameters, the feedback capacity can be achieved with delayed feedback. Finally, we show that the detectability condition is satisfied for scalar models and conjecture that it is true for MIMO models.

Item Type:Book Section
Related URLs:
URLURL TypeDescription ItemDiscussion Paper
Sabag, Oron0000-0002-7907-1463
Kostina, Victoria0000-0002-2406-7440
Additional Information:© 2022 IEEE.
Record Number:CaltechAUTHORS:20220804-765679000
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:116086
Deposited By: George Porter
Deposited On:09 Aug 2022 17:31
Last Modified:23 Dec 2022 20:25

Repository Staff Only: item control page