Details

Autor(en) / Beteiligte

• Liang, Yuanhui
• Lam, Chan-Tong
• Wu, Qingle
• Ng, Benjamin K.
• Im, Sio-Kei

Titel

Multi-Way Compression for Channel Neural Decoding with Quantization

Ist Teil von

• 2023 9th International Conference on Computer and Communications (ICCC), 2023, p.968-973

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• 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.