{"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/nirvawave-an-accurate-and-efficient-near","title":"NirvaWave: An Accurate and Efficient Near Field Wave Propagation Simulator for 6G and Beyond","arxiv_id":"2409.11293","date":"2024-09-17","proceeding":null,"authors":["Vahid Yazdnian","Atsutse Kludze","Yasaman Ghasempour"],"abstract":"The extended near-field range in future mm-Wave and sub-THz wireless networks demands a precise and efficient near-field channel simulator for understanding and optimizing wireless communications in this less-explored regime. This paper presents NirvaWave, a novel near-field channel simulator, built on scalar diffraction theory and Fourier principles, to provide precise wave propagation response in complex wireless mediums under custom user-defined transmitted EM signals. NirvaWave offers an interface for investigating novel near-field wavefronts, e.g., Airy beams, Bessel beams, and the interaction of mmWave and sub-THz signals with obstructions, reflectors, and scatterers. The simulation run-time in NirvaWave is orders of magnitude lower than its EM software counterparts that directly solve Maxwell Equations. Hence, NirvaWave enables a user-friendly interface for large-scale channel simulations required for developing new model-driven and data-driven techniques. We evaluated the performance of NirvaWave through direct comparison with EM simulation software. Finally, we have open-sourced the core codebase of NirvaWave in our GitHub repository (https://github.com/vahidyazdnian1378/NirvaWave).","url_abs":"https://arxiv.org/abs/2409.11293v2","url_pdf":"https://arxiv.org/pdf/2409.11293v2.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":"nirvawave-an-accurate-and-efficient-near","repo_url":"https://github.com/vahidyazdnian1378/nirvawave","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}