{"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/artificial-neural-network-modeling-for-path","title":"Artificial Neural Network Modeling for Path Loss Prediction in Urban Environments","arxiv_id":"1904.02383","date":"2019-04-04","proceeding":null,"authors":["Chanshin Park","Daniel K. Tettey","Han-Shin Jo"],"abstract":"Although various linear log-distance path loss models have been developed,\nadvanced models are requiring to more accurately and flexibly represent the\npath loss for complex environments such as the urban area. This letter proposes\nan artificial neural network (ANN) based multi-dimensional regression framework\nfor path loss modeling in urban environments at 3 to 6 GHz frequency band. ANN\nis used to learn the path loss structure from the measured path loss data which\nis a function of distance and frequency. The effect of the network architecture\nparameter (activation function, the number of hidden layers and nodes) on the\nprediction accuracy are analyzed. We observe that the proposed model is more\naccurate and flexible compared to the conventional linear model.","url_abs":"http://arxiv.org/abs/1904.02383v1","url_pdf":"http://arxiv.org/pdf/1904.02383v1.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":"artificial-neural-network-modeling-for-path","repo_url":"https://github.com/chanship/pathloss","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"artificial-neural-network-modeling-for-path","repo_url":"https://github.com/chanship72/pathloss","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"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}