{"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/distance-estimation-methods-for-a-practical","title":"Distance Estimation Methods for a Practical Macroscale Molecular Communication System","arxiv_id":"1909.12897","date":"2019-09-27","proceeding":null,"authors":[],"abstract":"Accurate estimation of the distance between the transmitter (TX) and the\nreceiver (RX) in molecular communication (MC) systems can provide faster and\nmore reliable communication. Existing theoretical models in the literature are\nnot suitable for distance estimation in a practical scenario. Furthermore,\nderiving an analytical model is not easy due to effects such as boundary\nconditions in the diffusion process, the initial velocity of the molecules and\nunsteady flows. Therefore, five different practical methods comprising three\nnovel data analysis based methods and two supervised machine learning (ML)\nmethods, Multivariate Linear Regression (MLR) and Neural Network Regression\n(NNR), are proposed for distance estimation at the RX side. In order to apply\nthe ML methods, a macroscale practical MC system, which consists of an electric\nsprayer without a fan, alcohol molecules, an alcohol sensor and a\nmicrocontroller, is established, and the received signals are recorded. A\nfeature extraction algorithm is proposed to utilize the measured signals as the\ninputs in ML methods. The numerical results show that the ML methods outperform\nthe data analysis based methods in the root mean square error sense with the\ncost of complexity. Moreover, the peak time based estimation, which is one of\nthe proposed data analysis based methods, yields better results with respect to\nthe other proposed four methods, as the distance increases. Given the\nexperimental data and fluid dynamics theory, a possible trajectory of the\nmolecules between the TX and RX is given. Our findings show that distance\nestimation performance is jointly affected by unsteady flows and the\nnon-linearity of the sensor. According to our findings based on fluid dynamics,\nit is evaluated that fluid dynamics should be taken into account for more\naccurate parameter estimation in practical macroscale MC systems.","url_abs":"http://arxiv.org/abs/1909.12897v1","url_pdf":"http://arxiv.org/pdf/1909.12897v1.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":"distance-estimation-methods-for-a-practical","repo_url":"https://github.com/fatihguelec/Distance-Estimation-in-Molecular-Communication","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"parameter-estimation","task_name":"parameter estimation"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}