A Learning-Based Approach to Address Complexity-Reliability Tradeoff in OS Decoders 2020 (Engelska)Ingår i: Conference Record - Asilomar Conference on Signals, Systems and Computers, IEEE Computer Society , 2020, s. 689-692Konferensbidrag, Publicerat paper (Refereegranskat)

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.

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IEEE Computer Society , 2020. s. 689-692

Nyckelord [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

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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

Konferens

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

Anmärkning

QC 20210906

Tillgänglig från: 2021-09-06 Skapad: 2021-09-06 Senast uppdaterad: 2023-04-05Bibliografiskt granskad

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Cavarec, BaptisteCelebi, Hasan BasriBengtsson, MatsSkoglund, Mikael

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Cavarec, BaptisteCelebi, Hasan BasriBengtsson, MatsSkoglund, Mikael

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