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Tibor Keresztfalvi

dblp:45/5861 · DBLP profile ↗
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5ranked-venue papers
4as first author
0since 2021 · last 2019
0000-0002-6881-6992ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 2 · 2 first-authorApplied, interdisciplinary, general and emerging computing · 2 · 2 first-authorArtificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
2 papers
Information theory · 100%

Topics — the 8 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information theory
channel capacity
0.822019
Semi-Robust Communications Over a Broadcast Channel · IEEE Trans. Inf. Theory 2019
Multiplexing Zero-Error and Rare-Error Communications Over a Noisy Channel · IEEE Trans. Inf. Theory 2019
Information theory
network information theory
0.822019
Semi-Robust Communications Over a Broadcast Channel · IEEE Trans. Inf. Theory 2019
Multiplexing Zero-Error and Rare-Error Communications Over a Noisy Channel · IEEE Trans. Inf. Theory 2019
Information theory › channel capacity
arbitrarily varying channel
0.412019
Semi-Robust Communications Over a Broadcast Channel · IEEE Trans. Inf. Theory 2019
Information theory › network information theory
broadcast channel
0.412019
Semi-Robust Communications Over a Broadcast Channel · IEEE Trans. Inf. Theory 2019
Information theory › channel capacity
capacity region
0.412019
Semi-Robust Communications Over a Broadcast Channel · IEEE Trans. Inf. Theory 2019
Information theory › channel capacity
feedback capacity
0.412019
Multiplexing Zero-Error and Rare-Error Communications Over a Noisy Channel · IEEE Trans. Inf. Theory 2019
Information theory › network information theory › multiuser capacity
rate region characterization
0.412019
Multiplexing Zero-Error and Rare-Error Communications Over a Noisy Channel · IEEE Trans. Inf. Theory 2019
Information theory › channel capacity
zero-error capacity
0.412019
Multiplexing Zero-Error and Rare-Error Communications Over a Noisy Channel · IEEE Trans. Inf. Theory 2019

Methods — techniques the papers use, named apart from their topics

deterministic-code capacity analysis · 0.4coding scheme design · 0.4
YearPublicationVenuePosition
2019 Multiplexing Zero-Error and Rare-Error Communications Over a Noisy Channel
abstract
Two independent data streams are to be transmitted over a noisy discrete memoryless channel with noiseless (ideal) feedback. Errors are tolerated only in the second stream, provided that they occur with vanishing probability. The rate of the error-free stream cannot, of course, exceed the channel's zero-error feedback capacity, and nor can the sum of the streams' rates exceed the channel's Shannon capacity. Using a suitable coding scheme, these necessary conditions are shown to characterize all the achievable rate pairs. Planning for the worst channel behavior-as is needed to achieve zero-error communication-and planning for the typical channel behavior-as is needed to communicate near the Shannon limit-are thus not incompatible. It is further shown that feedback may be beneficial for the multiplexing problem even on channels on which it does not increase the zero-error capacity.
Tibor Keresztfalvi, Amos Lapidoth
IEEE Trans. Inf. Theory1
2019 Semi-Robust Communications Over a Broadcast Channel
abstract
We establish the deterministic-code capacity region of a network with one transmitter and two receivers: an ordinary receiver and a robust receiver. The channel to the ordinary receiver is a given (known) discrete memoryless channel, whereas the channel to the robust receiver is an arbitrarily varying channel. Both receivers are required to decode the common message (the better-protected message), whereas only the ordinary receiver is required to decode the private message (the less-protected message). As in the single-user case, under the appropriate compactness and convexity conditions, the capacity region is either empty or else the intersection of the capacity regions of the broadcast channels that the various states induce.
Tibor Keresztfalvi, Amos Lapidoth
IEEE Trans. Inf. Theory1
2018 Partially-Robust Communications over a Noisy Channel
abstract
To study the fundamental limits on the joint transmission of data of different levels of sensitivity, we establish the deterministic-code capacity region of a network with one transmitter and two receivers: an “ordinary receiver” and a “robust receiver.” The channel to the ordinary receiver is a given (known) discrete memoryless channel, whereas the channel to the robust receiver is an arbitrarily varying channel. Both receivers are required to decode the “common message” (the more sensitive data), whereas only the ordinary receiver is required to decode the “private message” (the less sensitive data).
Tibor Keresztfalvi, Amos Lapidoth
ISIT1
2017 Multiplexing zero-error and rare-error communications over a noisy channel with feedback
abstract
Two independent data streams - the “zero-error stream” and the “rare-error stream” - are to be transmitted over a noisy discrete memoryless channel with feedback. Errors are tolerated only in the rare-error stream, provided that their probability tends to zero. Clearly the rate of the error-free stream cannot exceed the channel's zero-error feedback capacity, and the sum of the streams' rates cannot exceed the channel's Shannon capacity. Using a suitable coding scheme, these necessary conditions are shown to characterize all the achievable rate pairs. Planning for the worst - as is needed to achieve zero-error communication - and planning for the true channel - as is needed to communicate near the Shannon limit - are thus not incompatible.
Tibor Keresztfalvi, Amos Lapidoth
ISIT1
1995 Nonstandard conjunctions and implications in fuzzy logic
János C. Fodor, Tibor Keresztfalvi
Int. J. Approx. Reason.2