{"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/a-novel-multi-task-learning-empowered","title":"A Novel Multi-Task Learning Empowered Codebook Design for Downlink SCMA Networks","arxiv_id":"2204.08286","date":"2022-03-28","proceeding":null,"authors":["Qu Luo","Zilong Liu","Gaojie Chen","Yi Ma","Pei Xiao"],"abstract":"Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access (NOMA) scheme for the enabling of massive machine-type communication. In SCMA, the design of good sparse codebooks and efficient multiuser decoding have attracted tremendous research attention in the past few years. This paper aims to leverage deep learning to jointly design the downlink SCMA encoder and decoder with the aid of autoencoder. We introduce a novel end-to-end learning based SCMA (E2E-SCMA) design framework, under which improved sparse codebooks and low-complexity decoder are obtained. Compared to conventional SCMA schemes, our numerical results show that the proposed E2E-SCMA leads to significant improvements in terms of error rate and computational complexity.","url_abs":"https://arxiv.org/abs/2204.08286v1","url_pdf":"https://arxiv.org/pdf/2204.08286v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"a-novel-multi-task-learning-empowered","repo_url":"https://github.com/ethanlq/scma-codebook","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}