[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121737-en":3,"doc-seo-121737-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121737,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","AUTOMATIC DETECTION OF ACCESS POINT LOCATION USING MACHINE LEARNING AND PRIOR MODEL CALIBRATION","Accurate access point (AP) location is crucial for wireless coverage mapping and client location services, yet AP positions are often entered manually and become outdated when APs move, causing incorrect coverage maps and erroneous client location results. The presented techniques provide automated AP location discovery by leveraging prior model calibrations and machine learning to infer AP position from RSSI data or FTM protocol data. The method improves speed and accuracy over conventional trilateration.","Technical Disclosure Commons  \nDefensive Publications Series  \nSeptember 2023  \nAUTOMATIC DETECTION OF ACCESS POINT LOCATION USING MACHINE LEARNING AND PRIOR MODEL CALIBRATION  \nDavid Maluf Khanh V Nguyen Naveen Tyagi Jerome Henry  \nFollow this and additional works at: [https://www.tdcommons.org/dpubs_series](https://www.tdcommons.org/dpubs_series)  \nRecommended Citation  \nMaluf, David; V Nguyen, Khanh; Tyagi, Naveen; and Henry, Jerome, \"AUTOMATIC DETECTION OF ACCESS POINT LOCATION USING MACHINE LEARNING AND PRIOR MODEL CALIBRATION\", Technical Disclosure Commons,(September 26, 2023)  \n[https://www.tdcommons.org/dpubs_series/6277](https://www.tdcommons.org/dpubs_series/6277)  \nThis work is licensed under a Creative Commons Attribution 4.0 License.  \nThis Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive Publications Series by an authorized administrator of Technical Disclosure Commons.  \nAUTOMATIC DETECTION OF ACCESS POINT LOCATION USING MACHINE  \nLEARNING AND PRIOR MODEL CALIBRATION  \nAUTHORS:  \nDavid Maluf  \nKhanh V Nguyen  \nNaveen Tyagi  \nJerome Henry  \nABSTRACT  \nKnowledge of the correct location of an access point (AP) is of vital importance within a wireless ecosystem. Techniques are presented herein that support a new AP location identification method that uses prior model calibrations and machine learning (ML)  \ntechniques to detect an AP’s location using either received signal strength indicator (RSSI) data or fine time measurement (FTM) protocol data. Among other things, the new method  \nis faster and more accurate than conventional trilateration methods.  \nDETAILED DESCRIPTION  \nThe correct location of an access point (AP) is of critical importance to a wireless coverage map and for client location services within, for example, a cloud-based location services platform. To provide a sense of the involved scale, a network equipment vendor’s customer may have millions of APs in their network and such a customer may employ a vendor’s advanced digital network architecture manager to manage many millions ofAPs.  \nCurrently, to manage their APs a customer must manually enter the positions of those APs in a floor map, a process that is time consuming and prone to error. Customers also frequently move an AP to a new position without updating their advanced digital network architecture manager. Such inaccurate AP position information results in an incorrect wireless coverage map and an erroneous client location in the customer’s cloudbased location services platform and their connected mobile experience facilities.  \nThe ability to automatically detect the location of an AP would solve the abovedescribed challenges and, consequently, address many different needs including the detection of, and the issuance of a warning to a user regarding, the moving of an AP to anew location; the development of an estimate of the new location to which an AP has been  \n1 6952  \nPublished by Technical Disclosure Commons, 2023 2  \nmoved; the detection of the location of a new AP (that is, for example, added to a floor);  \nthe determination of whether an AP was incorrectly placed in a floor map; and the self  \nidentification of an AP’s location under Wi-Fi 6E.  \nTechniques are presented herein that support the automatic discovery of the location of an AP, based on the known positions of a small subset of APs, thus addressing the above-described challenges and needs.  \nThe presented techniques support an iterative method for automatically detecting the locations of the APs within the floor of a building or within an outdoor area. That method leverages an estimated distance between APs (such as may be obtained through, for example, a received signal strength indicator (RSSI) derivation); the known location of at least three anchor APs (that may be obtained through any method including a Gypsum module, manual input, or other means) ; and, optionally, the locations and ","cbCaiudp0ldBXztL","https://ap.wps.com/l/cbCaiudp0ldBXztL","pdf",881404,1,13,"English","en",105,"# Abstract\n# Detailed Description\n## Problem: Manual AP Positioning and Its Errors\n## Proposed Solution: Iterative AP Location Detection\n## RSSI Challenges and Calibration Approach\n## Machine Learning and Model-Based Distance Estimation\n## Path Loss Model Basis","[{\"question\":\"What problem does the document address?\",\"answer\":\"It addresses the difficulty and inaccuracy of manually entering and maintaining AP positions, which can lead to incorrect wireless coverage maps and erroneous client location results.\"},{\"question\":\"How does the proposed method determine an access point’s location?\",\"answer\":\"It uses an iterative approach that leverages distance estimates (from RSSI or other data), known anchor AP locations, and optionally obstacle geometry, combined with prior model calibration and machine learning.\"},{\"question\":\"Why are RSSI-based distance estimates challenging?\",\"answer\":\"RSSI strengths are affected by many unknown factors such as obstacle attenuation, multipath effects, transmission power, and antenna orientation/gain variations, causing potentially large distance estimation errors.\"}]","AUTOMATIC DETECTION OF ACCESS POINT LOCATION USING MACHINE LEARNING AND PRIOR MODEL CALIBRATION | 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problem does the document address?","Question",{"text":75,"@type":76},"It addresses the difficulty and inaccuracy of manually entering and maintaining AP positions, which can lead to incorrect wireless coverage maps and erroneous client location results.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method determine an access point’s location?",{"text":80,"@type":76},"It uses an iterative approach that leverages distance estimates (from RSSI or other data), known anchor AP locations, and optionally obstacle geometry, combined with prior model calibration and machine learning.",{"name":82,"@type":73,"acceptedAnswer":83},"Why are RSSI-based distance estimates challenging?",{"text":84,"@type":76},"RSSI strengths are affected by many unknown factors such as obstacle attenuation, multipath effects, transmission power, and antenna orientation/gain variations, causing potentially large distance estimation 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