A neural network lattice decoding algorithm

Mohammad-Reza Sadeghi, Farzane Amirzade, Daniel Panario, Amin Sakzad

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Neural network decoding algorithms are recently introduced by Nachmani et al. to decode high-density parity-check (HDPC) codes. In contrast with iterative decoding algorithms such as sum-product or min-sum algorithms in which the weight of each edge is set to 1, in the neural network decoding algorithms, the weight of every edge depends on its impact in the transmitted codeword. In this paper, we provide a novel feed-forward neural network lattice decoding algorithm suitable to decode lattices constructed based on Construction A, whose underlying codes have HDPC matrices. We first establish the concept of feed-forward neural network for HDPC codes and improve their decoding algorithms compared to Nachmani et al. We then apply our proposed decoder for a Construction A lattice with HDPC underlying code, for which the well-known iterative decoding algorithms show poor performances. The main advantage of our proposed algorithm is that instead of assigning and training weights for all edges, which turns out to be time-consuming especially for high-density parity-check matrices, we concentrate on edges which are present in most of 4-cycles and removing them gives a girth-6 Tanner graph. This approach, by slight modifications using updated LLRs instead of initial ones, simultaneously accelerates the training process and improves the error performance of our proposed decoding algorithm.

Original languageEnglish
Title of host publication2018 IEEE Information Theory Workshop (ITW)
EditorsQin Huang, Shenghao Yang
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781538635995, 9781538635988
ISBN (Print)9781538636008
DOIs
Publication statusPublished - 2018
EventInformation Theory Workshop 2018 - Guangzhou, China
Duration: 25 Nov 201829 Nov 2018
http://www.itw2018.org/

Conference

ConferenceInformation Theory Workshop 2018
Abbreviated titleITW 2018
CountryChina
CityGuangzhou
Period25/11/1829/11/18
Internet address

Keywords

  • Deep learning
  • Lattices
  • Tanner graph
  • Trellis graph

Cite this

Sadeghi, M-R., Amirzade, F., Panario, D., & Sakzad, A. (2018). A neural network lattice decoding algorithm. In Q. Huang, & S. Yang (Eds.), 2018 IEEE Information Theory Workshop (ITW) [8613440] Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ITW.2018.8613440
Sadeghi, Mohammad-Reza ; Amirzade, Farzane ; Panario, Daniel ; Sakzad, Amin. / A neural network lattice decoding algorithm. 2018 IEEE Information Theory Workshop (ITW). editor / Qin Huang ; Shenghao Yang. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018.
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abstract = "Neural network decoding algorithms are recently introduced by Nachmani et al. to decode high-density parity-check (HDPC) codes. In contrast with iterative decoding algorithms such as sum-product or min-sum algorithms in which the weight of each edge is set to 1, in the neural network decoding algorithms, the weight of every edge depends on its impact in the transmitted codeword. In this paper, we provide a novel feed-forward neural network lattice decoding algorithm suitable to decode lattices constructed based on Construction A, whose underlying codes have HDPC matrices. We first establish the concept of feed-forward neural network for HDPC codes and improve their decoding algorithms compared to Nachmani et al. We then apply our proposed decoder for a Construction A lattice with HDPC underlying code, for which the well-known iterative decoding algorithms show poor performances. The main advantage of our proposed algorithm is that instead of assigning and training weights for all edges, which turns out to be time-consuming especially for high-density parity-check matrices, we concentrate on edges which are present in most of 4-cycles and removing them gives a girth-6 Tanner graph. This approach, by slight modifications using updated LLRs instead of initial ones, simultaneously accelerates the training process and improves the error performance of our proposed decoding algorithm.",
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Sadeghi, M-R, Amirzade, F, Panario, D & Sakzad, A 2018, A neural network lattice decoding algorithm. in Q Huang & S Yang (eds), 2018 IEEE Information Theory Workshop (ITW)., 8613440, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, Information Theory Workshop 2018, Guangzhou, China, 25/11/18. https://doi.org/10.1109/ITW.2018.8613440

A neural network lattice decoding algorithm. / Sadeghi, Mohammad-Reza; Amirzade, Farzane; Panario, Daniel; Sakzad, Amin.

2018 IEEE Information Theory Workshop (ITW). ed. / Qin Huang; Shenghao Yang. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. 8613440.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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N2 - Neural network decoding algorithms are recently introduced by Nachmani et al. to decode high-density parity-check (HDPC) codes. In contrast with iterative decoding algorithms such as sum-product or min-sum algorithms in which the weight of each edge is set to 1, in the neural network decoding algorithms, the weight of every edge depends on its impact in the transmitted codeword. In this paper, we provide a novel feed-forward neural network lattice decoding algorithm suitable to decode lattices constructed based on Construction A, whose underlying codes have HDPC matrices. We first establish the concept of feed-forward neural network for HDPC codes and improve their decoding algorithms compared to Nachmani et al. We then apply our proposed decoder for a Construction A lattice with HDPC underlying code, for which the well-known iterative decoding algorithms show poor performances. The main advantage of our proposed algorithm is that instead of assigning and training weights for all edges, which turns out to be time-consuming especially for high-density parity-check matrices, we concentrate on edges which are present in most of 4-cycles and removing them gives a girth-6 Tanner graph. This approach, by slight modifications using updated LLRs instead of initial ones, simultaneously accelerates the training process and improves the error performance of our proposed decoding algorithm.

AB - Neural network decoding algorithms are recently introduced by Nachmani et al. to decode high-density parity-check (HDPC) codes. In contrast with iterative decoding algorithms such as sum-product or min-sum algorithms in which the weight of each edge is set to 1, in the neural network decoding algorithms, the weight of every edge depends on its impact in the transmitted codeword. In this paper, we provide a novel feed-forward neural network lattice decoding algorithm suitable to decode lattices constructed based on Construction A, whose underlying codes have HDPC matrices. We first establish the concept of feed-forward neural network for HDPC codes and improve their decoding algorithms compared to Nachmani et al. We then apply our proposed decoder for a Construction A lattice with HDPC underlying code, for which the well-known iterative decoding algorithms show poor performances. The main advantage of our proposed algorithm is that instead of assigning and training weights for all edges, which turns out to be time-consuming especially for high-density parity-check matrices, we concentrate on edges which are present in most of 4-cycles and removing them gives a girth-6 Tanner graph. This approach, by slight modifications using updated LLRs instead of initial ones, simultaneously accelerates the training process and improves the error performance of our proposed decoding algorithm.

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Sadeghi M-R, Amirzade F, Panario D, Sakzad A. A neural network lattice decoding algorithm. In Huang Q, Yang S, editors, 2018 IEEE Information Theory Workshop (ITW). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2018. 8613440 https://doi.org/10.1109/ITW.2018.8613440