@article{han_liu_li_yi_he_wang_2024, title={Accelerating Neural BP-Based Decoder Using Coded Distributed Computing}, volume={73}, ISSN={["1939-9359"]}, DOI={10.1109/TVT.2024.3391836}, number={9}, journal={IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY}, author={Han, Xuesong and Liu, Rui and Li, Yong and Yi, Chen and He, Jiguang and Wang, Ming}, year={2024}, month={Sep}, pages={13997–14002} }
 @article{wang_li_liu_guo_wu_lau_2023, title={Neural layered min-sum decoders for cyclic codes}, volume={61}, ISSN={["1874-4907"]}, DOI={10.1016/j.phycom.2023.102194}, abstractNote={This paper proposes a low-complexity neural network decoder based on the layered min-sum algorithm to decode cyclic codes. By generalizing the layered min-sum algorithm to its neural network counterpart, the number of network weights decreases while retaining a good error correction performance. The Bose–Chaudhuri–Hocquenghem (BCH) codes, quadratic residue (QR) codes, and punctured Reed–Muller (RM) codes are selected as three exemplary binary cyclic codes. Simulation results show that the proposed neural decoder achieves superior performance with less computational complexity compared with the state-of-the-art neural network decoder. Further, a neural decoder incorporating the modified random redundant decoding (mRRD) algorithm is investigated to approach the performance of maximum-likelihood decoding for some short codes.}, journal={PHYSICAL COMMUNICATION}, author={Wang, Ming and Li, Yong and Liu, Jianqing and Guo, Taolin and Wu, Huihui and Lau, Francis C. M.}, year={2023}, month={Dec} }