A Learning-Based Approach to Address Complexity-Reliability Tradeoff in OS Decoders 2020 (English)In: Conference Record - Asilomar Conference on Signals, Systems and Computers, IEEE Computer Society , 2020, p. 689-692Conference paper, Published paper (Refereed)

Abstract [en]

In this paper, we study the tradeoffs between complexity and reliability for decoding large linear block codes. We show that using artificial neural networks to predict the required order of an ordered statistics based decoder helps in reducing the average complexity and hence the latency of the decoder. We numerically validate the approach through Monte Carlo simulations.

Place, publisher, year, edition, pages

IEEE Computer Society , 2020. p. 689-692

Keywords [en]

Channel coding, learning, neural networks, order statistics decoder, Decoding, Monte Carlo methods, Average complexity, Learning-based approach, Linear block code, Ordered statistics, Reliability tradeoffs, Complex networks

National Category

Telecommunications

Identifiers

URN: urn:nbn:se:kth:diva-301234DOI: 10.1109/IEEECONF51394.2020.9443470ISI: 000681731800134Scopus ID: 2-s2.0-85102669025OAI: oai:DiVA.org:kth-301234DiVA, id: diva2:1591360

Conference

54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020, 1 November 2020 through 5 November 2020

Note

QC 20210906

Available from: 2021-09-06 Created: 2021-09-06 Last updated: 2023-04-05Bibliographically approved

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Cavarec, BaptisteCelebi, Hasan BasriBengtsson, MatsSkoglund, Mikael

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Cavarec, BaptisteCelebi, Hasan BasriBengtsson, MatsSkoglund, Mikael

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Information Science and Engineering

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