Low-Complexity LDPC Decoding Algorithm Based on Layered Vicinal Variable Node Scheduling
The informed dynamic scheduling (IDS) strategies for the low-density parity check (LDPC) decoding have shown superior performance in error correction and convergence speed, particularly those based on reliability measures and residual belief propagation (RBP). However, the search for the most unreli...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Hindawi Limited
2022-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/1407788 |
| _version_ | 1798025050783219712 |
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| author | Mhammed Benhayoun Mouhcine Razi Anas Mansouri Ali Ahaitouf |
| author_facet | Mhammed Benhayoun Mouhcine Razi Anas Mansouri Ali Ahaitouf |
| author_sort | Mhammed Benhayoun |
| collection | DOAJ |
| description | The informed dynamic scheduling (IDS) strategies for the low-density parity check (LDPC) decoding have shown superior performance in error correction and convergence speed, particularly those based on reliability measures and residual belief propagation (RBP). However, the search for the most unreliable variable nodes and the residual precomputation required for each iteration of the IDS-LDPC increases the complexity of the decoding process which becomes more sequential, making it hard to exploit the parallelism of signal processing algorithms available in multicore platforms. To overcome this problem, a new, low-complexity scheduling system, called layered vicinal variable nodes scheduling (LWNS) is presented in this paper. With this LWNS, each variable node is updated by exchanging intrinsic information with all its associated control and variable nodes before moving to the next variable node updating. The proposed scheduling strategy is fixed by a preprocessing step of the parity control matrix instead of calculation of the residuals values and by computation of the most influential variable node instead the most unreliable metric. It also allows the parallel processing of independent Tanner graph subbranches identified and grouped in layers. Our simulation results show that the LWNS BP have an attractive convergence rate and better error correction performance with low complexity when compared to previous IDS decoders under the white Gaussian noise channel (AWGN). |
| first_indexed | 2024-04-11T18:12:33Z |
| format | Article |
| id | doaj.art-f5ffde0742994cafad0d3218e3562c84 |
| institution | Directory Open Access Journal |
| issn | 1687-5605 |
| language | English |
| last_indexed | 2024-04-11T18:12:33Z |
| publishDate | 2022-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj.art-f5ffde0742994cafad0d3218e3562c842022-12-22T04:10:04ZengHindawi LimitedModelling and Simulation in Engineering1687-56052022-01-01202210.1155/2022/1407788Low-Complexity LDPC Decoding Algorithm Based on Layered Vicinal Variable Node SchedulingMhammed Benhayoun0Mouhcine Razi1Anas Mansouri2Ali Ahaitouf3Faculty of Sciences and TechnologyFaculty of Sciences and TechnologyNational School of Applied SciencesFaculty of Sciences and TechnologyThe informed dynamic scheduling (IDS) strategies for the low-density parity check (LDPC) decoding have shown superior performance in error correction and convergence speed, particularly those based on reliability measures and residual belief propagation (RBP). However, the search for the most unreliable variable nodes and the residual precomputation required for each iteration of the IDS-LDPC increases the complexity of the decoding process which becomes more sequential, making it hard to exploit the parallelism of signal processing algorithms available in multicore platforms. To overcome this problem, a new, low-complexity scheduling system, called layered vicinal variable nodes scheduling (LWNS) is presented in this paper. With this LWNS, each variable node is updated by exchanging intrinsic information with all its associated control and variable nodes before moving to the next variable node updating. The proposed scheduling strategy is fixed by a preprocessing step of the parity control matrix instead of calculation of the residuals values and by computation of the most influential variable node instead the most unreliable metric. It also allows the parallel processing of independent Tanner graph subbranches identified and grouped in layers. Our simulation results show that the LWNS BP have an attractive convergence rate and better error correction performance with low complexity when compared to previous IDS decoders under the white Gaussian noise channel (AWGN).http://dx.doi.org/10.1155/2022/1407788 |
| spellingShingle | Mhammed Benhayoun Mouhcine Razi Anas Mansouri Ali Ahaitouf Low-Complexity LDPC Decoding Algorithm Based on Layered Vicinal Variable Node Scheduling Modelling and Simulation in Engineering |
| title | Low-Complexity LDPC Decoding Algorithm Based on Layered Vicinal Variable Node Scheduling |
| title_full | Low-Complexity LDPC Decoding Algorithm Based on Layered Vicinal Variable Node Scheduling |
| title_fullStr | Low-Complexity LDPC Decoding Algorithm Based on Layered Vicinal Variable Node Scheduling |
| title_full_unstemmed | Low-Complexity LDPC Decoding Algorithm Based on Layered Vicinal Variable Node Scheduling |
| title_short | Low-Complexity LDPC Decoding Algorithm Based on Layered Vicinal Variable Node Scheduling |
| title_sort | low complexity ldpc decoding algorithm based on layered vicinal variable node scheduling |
| url | http://dx.doi.org/10.1155/2022/1407788 |
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