Demo Data from EDBT 2026 / reviewers in the wild / expert
Mordechai Mushkin
dblp:27/5478
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1989
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1 · 1 first-author
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
1 paper |
Information theory · 50% Coding theory · 50% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Coding theory › error-correcting codes
burst error channel |
0.0 | 1 | 1989 | Capacity and coding for the Gilbert-Elliot channels · IEEE Trans. Inf. Theory 1989 |
Information theory
channel capacity |
0.0 | 1 | 1989 | Capacity and coding for the Gilbert-Elliot channels · IEEE Trans. Inf. Theory 1989 |
Coding theory › error-correcting codes › convolutional codes › convolutional code decoding
decision feedback decoding |
0.0 | 1 | 1989 | Capacity and coding for the Gilbert-Elliot channels · IEEE Trans. Inf. Theory 1989 |
Information theory › channel capacity › state-dependent channel › finite-state channel
gilbert-elliott channel |
0.0 | 1 | 1989 | Capacity and coding for the Gilbert-Elliot channels · IEEE Trans. Inf. Theory 1989 |
Methods — techniques the papers use, named apart from their topics
random coding exponent · 0.0markov chain modeling · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1989 | Capacity and coding for the Gilbert-Elliot channelsabstractThe Gilbert-Elliott channel, a varying binary symmetric channel, with crossover probabilities determined by a binary-state Markov process, is treated. In general, such a channel has a memory that depends on the transition probabilities between the states. A method of calculating the capacity of this channel is introduced and applied to several examples, and the question of coding is addressed. In the conventional usage of varying channels, a code suitable for memoryless channels is used in conjunction with an interleaver, with the decoder considering the deinterleaved symbol stream as the output of a derived memoryless channel. The transmission rate is limited by the capacity of this memoryless channel, which is often considerably less than the capacity of the original channel. A decision-feedback decoding algorithm that completely recovers this capacity loss is introduced. It is shown that the performance of a system incorporating such an algorithm is determined by an equivalent genie-aided channel, the capacity of which equals that of the original channel. The calculated random coding exponent of the genie-aided channel indicates a considerable increase in the cutoff rate over that of the conventionally derived memoryless channel.> Mordechai Mushkin, Israel Bar-David |
IEEE Trans. Inf. Theory | 1 |