Journal
IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES
Volume E104A, Issue 10, Pages 1425-1429Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATION ENGINEERS
DOI: 10.1587/transfun.2020EAL2116
Keywords
alternating direction method of multipliers (ADMM); even vertex algorithm (EVA); high-density parity-check (HDPC) codes; check polytope projection
Categories
Funding
- Natural Science Foundation of Hubei Province [2020CFB474]
- Fundamental Research Funds for the Central Universities [CCNU20ZT002]
Ask authors/readers for more resources
For high-density parity-check (HDPC) codes, the traditional ADMM-LP decoder faces challenges, thus the HDPC-EVA algorithm is proposed to reduce complexity and improve performance. By introducing the even vertex algorithm (EVA) and the automorphism groups of codes, it is possible to achieve near maximum likelihood performance while enhancing decoding speed.
Linear programming (LP) decoding based on the alternating direction method of multipliers (ADMM) has proved to be effective for low-density parity-check (LDPC) codes. However, for high-density parity-check (HDPC) codes, the ADMM-LP decoder encounters two problems, namely a high-density check matrix in HDPC codes and a great number of pseudocodewords in HDPC codes' fundamental polytope. The former problem makes the check polytope projection extremely complex, and the latter one leads to poor frame error rates (FER) performance. To address these issues, we introduce the even vertex algorithm (EVA) into the ADMM-LP decoding algorithm for HDPC codes, named as HDPC-EVA. HDPC-EVA can reduce the complexity of the projection process and improve the FER performance. We further enhance the proposed decoder by the automorphism groups of codes, creating diversity in the parity-check matrix. The simulation results show that the proposed decoder is capable of cutting down the average decoding time for each iteration by 30%-60%, as well as achieving near maximum likelihood (ML) performance on some BCH codes.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available