A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
Source abstract
Abstract
The conventional soft decision decoding (SDD) methods require various hard decision decoders (HDDs) based on different codes or re-manipulate the generator matrix by the complicated Gaussian elimination technique according to the bit reliability. This paper presents a general multi-class neural network (NN)-based decoder for the short linear block codes, where no HDD and Gaussian elimination are required once the NN is constructed. This network architecture performs multi-classification to select the messages with high occurrence probabilities and chooses the best codeword on a maximum likelihood basis. Simulation results show that the developed approach outperforms the existing deep neural network (DNN)-based decoders in terms of decoding time and bit error rate (BER). The error-correcting performance is also superior to the conventional Chase-II algorithm and is close to the ordered statistics decoding (OSD) in most cases. For Bose–Chaudhuri–Hocquenghem (BCH) codes, the SNR is improved by 1dB to 4dB as the BER is 10−4. For the (23, 12) quadratic residue (QR) code, the SNR is improved by 2dB when the BER is 10−3. The developed NN-based decoder is quite general and applicable to various short linear block codes with good BER performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Error Correcting Code Techniques · Advanced Wireless Communication Techniques · Coding theory and cryptography
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
Department Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
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