Time-Invariant Feedback Strategies Do Not Increase Capacity of AGN Channels Driven by Stable and Certain Unstable Autoregressive Noise
Charalambos D. Charalambous, Christos Kourtellaris, Sergey Loyka

TL;DR
This paper characterizes the capacity of AGN channels with autoregressive noise under time-invariant feedback, revealing regimes where feedback increases capacity and deriving new formulas for both stable and unstable AR noise models.
Contribution
It provides new closed-form capacity formulas and bounds for AGN channels with AR noise, including unstable cases, and identifies when feedback enhances capacity.
Findings
Feedback increases capacity for unstable AR noise when power exceeds a threshold.
Feedback does not increase capacity for stable AR noise or low power regimes.
New capacity formulas differ from existing literature, showing multiple capacity regimes.
Abstract
The capacity of additive Gaussian noise (AGN) channels, , , with time-invariant channel input feedback strategies, is characterized and conditions are identified for entropy rates, and limit of average power to exist, when the noise is described by {\it stable and unstable} autoregressive models, AR, , where , , is a zero mean, variance , independent Gaussian sequence, independent of . For stable AR the conditions are necessary and sufficient for asymptotic stationarity of the processes . New closed form capacity formulas and lower bounds are derived, for the AR noise, which are fundamentally…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsWireless Communication Security Techniques · stochastic dynamics and bifurcation · Distributed Sensor Networks and Detection Algorithms
