{"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/angle-delay-profile-based-and-timestamp-aided","title":"Angle-Delay Profile-Based and Timestamp-Aided Dissimilarity Metrics for Channel Charting","arxiv_id":"2308.09539","date":"2023-08-18","proceeding":null,"authors":["Phillip Stephan","Florian Euchner","Stephan ten Brink"],"abstract":"Channel charting is a self-supervised learning technique whose objective is to reconstruct a map of the radio environment, called channel chart, by taking advantage of similarity relationships in high-dimensional channel state information. We provide an overview of processing steps and evaluation methods for channel charting and propose a novel dissimilarity metric that takes into account angular-domain information as well as a novel deep learning-based metric. Furthermore, we suggest a method to fuse dissimilarity metrics such that both the time at which channels were measured as well as similarities in channel state information can be taken into consideration while learning a channel chart. By applying both classical and deep learning-based manifold learning to a dataset containing sub-6GHz distributed massive MIMO channel measurements, we show that our metrics outperform previously proposed dissimilarity measures. The results indicate that the new metrics improve channel charting performance, even under non-line-of-sight conditions.","url_abs":"https://arxiv.org/abs/2308.09539v2","url_pdf":"https://arxiv.org/pdf/2308.09539v2.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":"angle-delay-profile-based-and-timestamp-aided","repo_url":"https://github.com/jeija/espargos-passive-channelcharting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"angle-delay-profile-based-and-timestamp-aided","repo_url":"https://github.com/jeija/toa-aoa-augmented-channelcharting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"angle-delay-profile-based-and-timestamp-aided","repo_url":"https://github.com/phillipstephan/channelcharting-channelprediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","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}