Application of Tensor Decomposition to Reduce the Complexity of Neural Min-Sum Channel Decoding Algorithm
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Published:2023-02-09
Issue:4
Volume:13
Page:2255
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Wu Qingle1ORCID, Ng Benjamin K.1ORCID, Liang Yuanhui1, Lam Chan-Tong1ORCID, Ma Yan12ORCID
Affiliation:
1. Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China 2. BUPT Network Information Center, Beijing University of Posts and Telecommunications, Beijing 100876, China
Abstract
Channel neural decoding is very promising as it outperforms the traditional channel decoding algorithms. Unfortunately, it still faces the disadvantage of high computational complexity and storage complexity compared with the traditional decoding algorithms. In this paper, we propose that low rank decomposition techniques based on tensor train decomposition and tensor ring decomposition can be utilized in neural offset min-sum (NOMS) and neural scale min-sim (NSMS) decoding algorithms. The experiment results show that the proposed two algorithms achieve near state-of-the-art performance with low complexity.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference25 articles.
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