@inproceedings{86b701e4ccd94197a0e2440974069bec,
title = "Multi-Way Compression for Channel Neural Decoding with Quantization",
abstract = "The performance of model-driven channel neural decoding has surpassed that of traditional channel decoding algorithms, but at a higher complexity, making it difficult to implement on resource-constrained communication hardware. In this paper, we propose a quantization scheme for model-driven channel neural decoding, and combine the quantization scheme with TR decomposition and weight sharing algorithms to form different types of multi-way compression methods. Experimental results on LDPC, BCH and Hamming codes show that the proposed quantization and multi-way compression methods can effectively reduce the complexity of channel neural decoding without significant performance degradation.",
keywords = "channel neural decoding, model-driven, multi-way compression, quantization",
author = "Yuanhui Liang and Lam, {Chan Tong} and Qingle Wu and Ng, {Benjamin K.} and Im, {Sio Kei}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 9th International Conference on Computer and Communications, ICCC 2023 ; Conference date: 08-12-2023 Through 11-12-2023",
year = "2023",
doi = "10.1109/ICCC59590.2023.10507411",
language = "English",
series = "2023 9th International Conference on Computer and Communications, ICCC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "968--973",
booktitle = "2023 9th International Conference on Computer and Communications, ICCC 2023",
address = "United States",
}