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journal article

Machine Learning-Aided Efficient Decoding of Reed-Muller Subcodes

Jamali, Mohammad Vahid
•
Liu, Xiyang
•
Makkuva, Ashok Vardhan  
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2023
IEEE Journal on Selected Areas in Information Theory

Reed-Muller (RM) codes achieve the capacity of general binary-input memoryless symmetric channels and are conjectured to have a comparable performance to that of random codes in terms of scaling laws. However, such results are established assuming maximum-likelihood decoders for general code parameters. Also, RM codes only admit limited sets of rates. Efficient decoders such as successive cancellation list (SCL) decoder and recently-introduced recursive projection-aggregation (RPA) decoders are available for RM codes at finite lengths. In this paper, we focus on subcodes of RM codes with flexible rates. We first extend the RPA decoding algorithm to RM subcodes. To lower the complexity of our decoding algorithm, referred to as subRPA, we investigate different approaches to prune the projections. Next, we derive the soft-decision based version of our algorithm, called soft-subRPA, that not only improves upon the performance of subRPA but also enables a differentiable decoding algorithm. Building upon the soft-subRPA algorithm, we then provide a framework for training a machine learning (ML) model to search for good sets of projections that minimize the decoding error rate. Training our ML model enables achieving very close to the performance of full-projection decoding with a significantly smaller number of projections. We also show that the choice of the projections in decoding RM subcodes matters significantly, and our ML-aided projection pruning scheme is able to find a good selection, i.e., with negligible performance degradation compared to the full-projection case, given a reasonable number of projections.

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Type
journal article
DOI
10.1109/JSAIT.2023.3298362
Scopus ID

2-s2.0-85188495553

Author(s)
Jamali, Mohammad Vahid
•
Liu, Xiyang
•
Makkuva, Ashok Vardhan  
•
Mahdavifar, Hessam
•
Oh, Sewoong
•
Viswanath, Pramod
Date Issued

2023

Published in
IEEE Journal on Selected Areas in Information Theory
Volume

4

Start page

260

End page

275

Subjects

low-complexity decoding

•

machine learning

•

projection pruning

•

recursive projection-aggregation (RPA) decoding

•

Reed-muller (RM) codes

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LINX  
FunderFunding(s)Grant NumberGrant URL

National Science Foundation

CCF- 1941633,CCF-2312752,CCF-2312753,CNS-2002932

ONR

W911NF- 18-1-0332

Available on Infoscience
January 26, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/244546
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