{"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/mmrapid-machine-learning-assisted-noncoherent","title":"mmRAPID: Machine Learning assisted Noncoherent Compressive Millimeter-Wave Beam Alignment","arxiv_id":"2007.12060","date":"2020-07-23","proceeding":null,"authors":["Han Yan","Benjamin W. Domae","Danijela Cabric"],"abstract":"Millimeter-wave communication has the potential to deliver orders of magnitude increases in mobile data rates. A key design challenge is to enable rapid beam alignment with phased arrays. Traditional millimeter-wave systems require a high beam alignment overhead, typically an exhaustive beam sweep, to find the beam direction with the highest beamforming gain. Compressive sensing is a promising framework to accelerate beam alignment. However, model mismatch from practical array hardware impairments poses a challenge to its implementation. In this work, we introduce a neural network assisted compressive beam alignment method that uses noncoherent received signal strength measured by a small number of pseudorandom sounding beams to infer the optimal beam steering direction. We experimentally showcase our proposed approach with a 60GHz 36-element phased array in a suburban line-of-sight environment. The results show that our approach achieves post alignment beamforming gain within 1dB margin compared to an exhaustive search with 90.2 percent overhead reduction. Compared to purely model-based noncoherent compressive beam alignment, our method has 75 percent overhead reduction.","url_abs":"https://arxiv.org/abs/2007.12060v1","url_pdf":"https://arxiv.org/pdf/2007.12060v1.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":"mmrapid-machine-learning-assisted-noncoherent","repo_url":"https://github.com/bdomae/mmRAPID","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"compressive-sensing","task_name":"Compressive Sensing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}