Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

A New Dynamic Scheduling Scheme for Hierarchical Decoding of 5G LDPC Codes Based on Edge Information

Yaopeng Gong, Dongming Xu, Kun Zhang

Published Sep 20, 2024• 0 citations
Publisher

Abstract

Quasi-cyclic low-density parity-check codes (QC-LDPC) are selected as the channel coding scheme for the data channel of the new radio (NR) enhanced mobile broadband (eMBB) in fifth-generation (5G) mobile communication technology. Traditional layered scheduling strategies used in 5G LDPC codes improve decoding convergence speed, but fixed scheduling schemes cannot fully utilize dynamic changes in decoded messages. To address this issue, this paper proposes a layered scheduling scheme for 5G LDPC codes based on edge information. By prioritizing updates to layers with minimal changes in edge information, this method reduces decoding complexity and accelerates convergence with minimal performance loss. Simulation results demonstrate that this approach enhances decoding performance by approximately 0.05dB compared to other improved hierarchical scheduling strategies.

DOI
10.1145/3703935.3704001

Related papers

Two Effective Scheduling Schemes for Layered Belief Propagation of 5G LDPC Codes

Bingbing Wang, Yan Zhu, Jing Kang

Low-density parity-check (LDPC) codes have been chosen for the fifth-generation (5G) new radio (NR) enhanced Mobile Broad Band (eMBB) data channel. Since NR LDPC codes puncture the first two block columns of the parity-check matrix, the decoding complexity is reduced, but it slows down the convergence rate. In this letter, we propose a fixed schedule that decodes the layers with the least-punctured edges and those with the highest-degree (LPHD), which has much faster convergence speed than conventional schemes. The fixed scheduling schemes cannot take full advantage of the dynamic changes of decoding messages. As a result, the residual-based layered belief propagation (RB-LBP) is proposed to rearrange the layers dynamically among different iterations. Compared with some informed dynamic scheduling (IDS) strategies, RB-LBP reduces the decoding complexity with slight performance loss.

IEEE Communications Letters•Apr 30, 2020•40 citations

A Novel Base Graph Based Static Scheduling Scheme for Layered Decoding of 5G LDPC Codes

Kuangda Tian, Hao Wang

Quasi-cyclic low-density parity-check (QC-LDPC) codes have been selected as the channel coding scheme for 5G new radio (NR) data channel. In this letter, a novel base graph based static scheduling (BGSS) method is proposed for layered decoding of 5G LDPC codes to accelerate decoding convergence and reduce the average number of iterations (ANI). In the proposed BGSS method, the update order of layers is determined based on row weight, column weight and number of punctured non-zero elements of BG matrix. Three optimization rules are included in BGSS to determine the order: 1) The layer with less punctured non-zero element is updated firstly. 2) The layer with relatively small row weight is updated firstly. 3) The layer which can utilize more recent information is updated firstly. The first rule has the highest priority and the third rule has the lowest priority. Simulation results show that compared with other scheduling method, the proposed method reduces about 1.1 ANI and obtains about 0.05dB performance gain for given LDPC codes.

IEEE Communications Letters•May 2, 2022•19 citations

Reduced Complexity Dynamic Schedule for Layered Decoding of LDPC Codes

Xinle Jia, Yangcan Zhou, Zijian Qin +2 more

Low-density parity-check (LDPC) codes have been selected as the coding scheme of the data channel in 5G communication. To enhance the performance of conventional layered decoding of LDPC codes, various dynamic schedules have been proposed recently. However, these dynamic schedules suffer from high complexity. In this paper, we propose a reduced complexity dynamic layered schedule for LDPC codes, where the layer to be updated is determined based on the residual of each layer. Moreover, an adjustable parameter is introduced to achieve a better performance-complexity trade-off. Simulation results show that the proposed method maintains excellent error-correction performance while the computational complexity is greatly reduced, compared to the prior dynamic schedules.

IEEE Transactions on Vehicular Technology•Mar 14, 2025•3 citations

Improved Informed Dynamic Scheduling Strategies for 5G LDPC Codes

Kunyang Li, Huai-Yi Li, Li Juan DENG +3 more

In this paper, we propose two new scheduling algorithms to address the issues of existing informed dynamic scheduling (IDS) decoding algorithms of short low-density parity-check (LDPC) codes for the fifth-generation (5G) new radio (NR). The first issue is the complexity problem, where the high complexity of IDS makes hardware implementation difficult. By considering the impact of puncturing for the first two columns of base matrices, the proposed least-punctured and sum-residual-based (LPSRB) algorithm can reduce decoding complexity. The second issue is the significant degradation of decoding performance when the code length is short. By fully utilizing residuals, the proposed novel residual belief propagation (NRBP) algorithm allocates a significant amount of decoding resources to the less convergent edges. Simulation results demonstrate that NRBP achieves better frame error rate (FER) performance for short LDPC codes.

IEEE International Conference on Communication Technology (ICCT)•Oct 20, 2023•1 citation

Enhanced Adaptive Normalized Min-Sum Algorithm for Layered Scheduling of 5G-NR LDPC codes

Ziqi Zhou, Kewu Peng, Aleksei E. Krylov +5 more

5G new radio low density parity check (5G-NR LDPC) codes specified by the 3rd generation partnership project (3GPP), a representative class of multi-edge-type, quasi-cyclic and raptor-like LDPC codes with rate-compatible and length-scalable capability, could meet with high-throughput demands. The corresponding decoding algorithm and implementation should support low complexity, high performance and scaleinvariance to channel variations and receiver imperfections. Flooding scheduling is usually used for the prevalent decoding algorithms and architecture of LDPC codes, but it is inefficient and inflexible compared with layered scheduling. This paper proposes a new simplification method where the sub-groups with similar fitting functions of normalization factors are incorporated into the same type, and enhanced adaptive normalized min-sum algorithm (enhanced ANMSA) for layered scheduling is further proposed for the implementation of 5G-NR LDPC codes. Based on the new simplification method, the proposed algorithm defines three types of check nodes for base graph 2 (BG2) in 5G-NR LDPC codes, and then obtains three look-up tables for BG2 by training. Simulation results demonstrate that the proposed algorithm can achieve excellent performance for 5G-NR LDPC codes of different rates and lengths over Binary-input AWGN channel and Rayleigh channel.

IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)•Oct 27, 2020•9 citations

Enhanced Informed Dynamic BP Decoding Scheduling Strategies for 5G NR LDPC Codes

Tofar C.-Y. Chang, I-Hsiang Lee, Pin-Han Wang +2 more

We present an enhanced informed dynamic scheduling (IDS) strategy to avoid propagations of unreliable messages in the residual belief-propagation (RBP) algorithm for decoding low-density parity-check (LDPC) codes. To reduce the resource needed for updating the degree-one variable nodes (VNs) in an RBP-based algorithm for decoding 5G New Radio (NR) LDPC codes, we further introduce a process consisting of several simple procedures for managing the resource consumption. Simulation results demonstrate that our RBP algorithm based on the proposed IDS strategy outperforms existing RBP-based algorithms at the early decoding iterations. By using our degree-one VN resource managing approach, the performance of both the proposed and the existing decoders is significantly improved in decoding the 5G NR codes.

IEEE Vehicular Technology Conference•Sep 1, 2022•1 citation
Quasi-cyclic low-density parity-check codes (QC-LDPC) are selected as the channel coding scheme for the data channel of the new radio (NR) enhanced mobile broadband (eMBB) in fifth-generation (5G) mobile communication technology. Traditional layered scheduling strategies used in 5G LDPC codes improve decoding convergence speed, but fixed scheduling schemes cannot fully utilize dynamic changes in decoded messages. To address this issue, this paper proposes a layered scheduling scheme for 5G LDPC codes based on edge information. By prioritizing updates to layers with minimal changes in edge information, this method reduces decoding complexity and accelerates convergence with minimal performance loss. Simulation results demonstrate that this approach enhances decoding performance by approximately 0.05dB compared to other improved hierarchical scheduling strategies.
Low-density parity-check (LDPC) codes have been chosen for the fifth-generation (5G) new radio (NR) enhanced Mobile Broad Band (eMBB) data channel. Since NR LDPC codes puncture the first two block columns of the parity-check matrix, the decoding complexity is reduced, but it slows down the convergence rate. In this letter, we propose a fixed schedule that decodes the layers with the least-punctured edges and those with the highest-degree (LPHD), which has much faster convergence speed than conventional schemes. The fixed scheduling schemes cannot take full advantage of the dynamic changes of decoding messages. As a result, the residual-based layered belief propagation (RB-LBP) is proposed to rearrange the layers dynamically among different iterations. Compared with some informed dynamic scheduling (IDS) strategies, RB-LBP reduces the decoding complexity with slight performance loss.
Quasi-cyclic low-density parity-check (QC-LDPC) codes have been selected as the channel coding scheme for 5G new radio (NR) data channel. In this letter, a novel base graph based static scheduling (BGSS) method is proposed for layered decoding of 5G LDPC codes to accelerate decoding convergence and reduce the average number of iterations (ANI). In the proposed BGSS method, the update order of layers is determined based on row weight, column weight and number of punctured non-zero elements of BG matrix. Three optimization rules are included in BGSS to determine the order: 1) The layer with less punctured non-zero element is updated firstly. 2) The layer with relatively small row weight is updated firstly. 3) The layer which can utilize more recent information is updated firstly. The first rule has the highest priority and the third rule has the lowest priority. Simulation results show that compared with other scheduling method, the proposed method reduces about 1.1 ANI and obtains about 0.05dB performance gain for given LDPC codes.
Low-density parity-check (LDPC) codes have been selected as the coding scheme of the data channel in 5G communication. To enhance the performance of conventional layered decoding of LDPC codes, various dynamic schedules have been proposed recently. However, these dynamic schedules suffer from high complexity. In this paper, we propose a reduced complexity dynamic layered schedule for LDPC codes, where the layer to be updated is determined based on the residual of each layer. Moreover, an adjustable parameter is introduced to achieve a better performance-complexity trade-off. Simulation results show that the proposed method maintains excellent error-correction performance while the computational complexity is greatly reduced, compared to the prior dynamic schedules.
In this paper, we propose two new scheduling algorithms to address the issues of existing informed dynamic scheduling (IDS) decoding algorithms of short low-density parity-check (LDPC) codes for the fifth-generation (5G) new radio (NR). The first issue is the complexity problem, where the high complexity of IDS makes hardware implementation difficult. By considering the impact of puncturing for the first two columns of base matrices, the proposed least-punctured and sum-residual-based (LPSRB) algorithm can reduce decoding complexity. The second issue is the significant degradation of decoding performance when the code length is short. By fully utilizing residuals, the proposed novel residual belief propagation (NRBP) algorithm allocates a significant amount of decoding resources to the less convergent edges. Simulation results demonstrate that NRBP achieves better frame error rate (FER) performance for short LDPC codes.
5G new radio low density parity check (5G-NR LDPC) codes specified by the 3rd generation partnership project (3GPP), a representative class of multi-edge-type, quasi-cyclic and raptor-like LDPC codes with rate-compatible and length-scalable capability, could meet with high-throughput demands. The corresponding decoding algorithm and implementation should support low complexity, high performance and scaleinvariance to channel variations and receiver imperfections. Flooding scheduling is usually used for the prevalent decoding algorithms and architecture of LDPC codes, but it is inefficient and inflexible compared with layered scheduling. This paper proposes a new simplification method where the sub-groups with similar fitting functions of normalization factors are incorporated into the same type, and enhanced adaptive normalized min-sum algorithm (enhanced ANMSA) for layered scheduling is further proposed for the implementation of 5G-NR LDPC codes. Based on the new simplification method, the proposed algorithm defines three types of check nodes for base graph 2 (BG2) in 5G-NR LDPC codes, and then obtains three look-up tables for BG2 by training. Simulation results demonstrate that the proposed algorithm can achieve excellent performance for 5G-NR LDPC codes of different rates and lengths over Binary-input AWGN channel and Rayleigh channel.
We present an enhanced informed dynamic scheduling (IDS) strategy to avoid propagations of unreliable messages in the residual belief-propagation (RBP) algorithm for decoding low-density parity-check (LDPC) codes. To reduce the resource needed for updating the degree-one variable nodes (VNs) in an RBP-based algorithm for decoding 5G New Radio (NR) LDPC codes, we further introduce a process consisting of several simple procedures for managing the resource consumption. Simulation results demonstrate that our RBP algorithm based on the proposed IDS strategy outperforms existing RBP-based algorithms at the early decoding iterations. By using our degree-one VN resource managing approach, the performance of both the proposed and the existing decoders is significantly improved in decoding the 5G NR codes.