Feedback Does Not Increase the Capacity of Compound Channels with Additive Noise
Sergey Loyka, Charalambos D. Charalambous

TL;DR
This paper demonstrates that for a broad class of discrete compound channels with additive noise, neither feedback nor full transmitter channel state information increases capacity, extending previous results and identifying conditions for the strong converse.
Contribution
It extends the understanding of capacity limits in compound channels with additive noise, showing feedback and CSI do not increase capacity under certain conditions.
Findings
Feedback does not increase capacity in the considered setting.
Full transmitter CSI does not increase capacity under mild conditions.
Conditions for the strong converse to hold are characterized.
Abstract
A discrete compound channel with memory is considered, where no stationarity, ergodicity or information stability is required, and where the uncertainty set can be arbitrary. When the discrete noise is additive but otherwise arbitrary and there is no cost constraint on the input, it is shown that the causal feedback does not increase the capacity. This extends the earlier result obtained for general single-state channels with full transmitter (Tx) channel state information (CSI) to the compound setting. It is further shown that, for this compound setting and under a mild technical condition on the additive noise, the addition of the full Tx CSI does not increase the capacity either, so that the worst-case and compound channel capacities are the same. This can also be expressed as a saddle-point in the information-theoretic game between the transmitter (who selects the input…
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 · Distributed Sensor Networks and Detection Algorithms · Energy Harvesting in Wireless Networks
