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Ravi Sivasankaran

dblp:47/10924 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2001
—ORCID · none

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

Computer networks · 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
Coding theory · 100%

Topics — the 7 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Coding theory › error-correcting codes
convolutional codes
0.012001
Twin-stack decoding of recursive systematic convolutional codes · IEEE Trans. Commun. 2001
Coding theory › error-correcting codes › decoding
iterative decoding
0.012001
Twin-stack decoding of recursive systematic convolutional codes · IEEE Trans. Commun. 2001
Coding theory › error-correcting codes › convolutional codes › convolutional encoders
recursive systematic convolutional codes
0.012001
Twin-stack decoding of recursive systematic convolutional codes · IEEE Trans. Commun. 2001
Coding theory › error-correcting codes › decoding › iterative decoding
soft-input soft-output decoding
0.012001
Twin-stack decoding of recursive systematic convolutional codes · IEEE Trans. Commun. 2001
Coding theory › channel coding
turbo codes
0.012001
Twin-stack decoding of recursive systematic convolutional codes · IEEE Trans. Commun. 2001
Coding theory › error-correcting codes › decoding
sequential decoding
0.012001
Twin-stack decoding of recursive systematic convolutional codes · IEEE Trans. Commun. 2001
Coding theory › error-correcting codes › decoding › sequential decoding
stack decoding
0.012001
Twin-stack decoding of recursive systematic convolutional codes · IEEE Trans. Commun. 2001

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

twin-stack decoding · 0.0maximum a posteriori · 0.0BCJR algorithm · 0.0
YearPublicationVenuePosition
2001 Twin-stack decoding of recursive systematic convolutional codes
abstract
We present a method for soft-in/soft-out sequential decoding of recursive systematic convolutional codes. The proposed decoder, the twin-stack decoder, is an extension of the well-known ZJ stack decoder, and it uses two stacks. The use of the two stacks lends itself to the generation of soft outputs, and the decoder is easily incorporated into the iterative "turbo" configuration. Under thresholded decoding, it is observed that the decoder is capable of achieving near-maximum a posteriori bit-error rate performance at moderate to high signal-to-noise ratios (SNRs). Also, in the iterative (turbo) configuration, at moderate SNRs (above 2.0 dB), the performance of the proposed decoder is within 1.5 dB of the BCJR algorithm for a 16-state, R=1/3, recursive code, but this difference narrows progressively at higher SNRs. The complexity of the decoder asymptotically decreases (with SNR) as 1/(number of states), providing a good tradeoff between computational burden and performance. The proposed decoder is also within 1.0 dB of other well-known suboptimal soft-out decoding techniques.
Ravi Sivasankaran, Steven W. McLaughlin
IEEE Trans. Commun.1