Accelerating Neural BP-Based Decoder Using Coded Distributed Computing

While neural BP-based (NBP) decoders exhibit superior error correction performance compared to belief-propagation (BP) decoders, the NBP decoder's high computational and memory requirements impede its practical deployment in communication systems. To overcome this challenge, we propose a Coded...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on vehicular technology Jg. 73; H. 9; S. 13997 - 14002
Hauptverfasser: Han, Xuesong, Liu, Rui, Li, Yong, Yi, Chen, He, Jiguang, Wang, Ming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.09.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
BP-based decoder
Codes
coding theory
Communications systems
Complexity theory
Decoders
Decoding
Distributed computing
Distributed processing
Encoding
Error correction
neural network
Neurons
straggler resilience
ISSN:0018-9545, 1939-9359
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:While neural BP-based (NBP) decoders exhibit superior error correction performance compared to belief-propagation (BP) decoders, the NBP decoder's high computational and memory requirements impede its practical deployment in communication systems. To overcome this challenge, we propose a Coded Neural BP (CNBP) scheme to accelerate the NBP decoder in distributed environments, while considering storage constraints and providing resilience to stragglers. The key idea is to reformulate the primary operations of the NBP decoder as matrix-vector multiplications by introducing weight matrices and transformations. Based on this, the acceleration of the NBP decoder is achieved by speeding up matrix-vector multiplications using coded distributed computing. Extensive experiments conducted on Amazon EC2 cluster demonstrate that CNBP achieves notable acceleration and scalability performance without any loss in error correction performance.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2024.3391836