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Mordechai Mushkin

dblp:27/5478 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Coding theory › error-correcting codes
burst error channel
0.011989
Capacity and coding for the Gilbert-Elliot channels · IEEE Trans. Inf. Theory 1989
Information theory
channel capacity
0.011989
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
0.011989
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
0.011989
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
YearPublicationVenuePosition
1989 Capacity and coding for the Gilbert-Elliot channels
abstract
The 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. Theory1