{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/performance-evaluation-of-channel-decoding","title":"Performance Evaluation of Channel Decoding With Deep Neural Networks","arxiv_id":"1711.00727","date":"2017-11-01","proceeding":null,"authors":["Wei Lyu","Zhaoyang Zhang","Chunxu Jiao","Kangjian Qin","Huazi Zhang"],"abstract":"With the demand of high data rate and low latency in fifth generation (5G),\ndeep neural network decoder (NND) has become a promising candidate due to its\ncapability of one-shot decoding and parallel computing. In this paper, three\ntypes of NND, i.e., multi-layer perceptron (MLP), convolution neural network\n(CNN) and recurrent neural network (RNN), are proposed with the same parameter\nmagnitude. The performance of these deep neural networks are evaluated through\nextensive simulation. Numerical results show that RNN has the best decoding\nperformance, yet at the price of the highest computational overhead. Moreover,\nwe find there exists a saturation length for each type of neural network, which\nis caused by their restricted learning abilities.","url_abs":"http://arxiv.org/abs/1711.00727v2","url_pdf":"http://arxiv.org/pdf/1711.00727v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"performance-evaluation-of-channel-decoding","repo_url":"https://github.com/levylv/deep-neural-network-decoder","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.00727","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}