{"paper":{"title":"A Machine Learning Framework for Large-Scale Static Wireless Mesh Networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Julia Andrusenko","submitted_at":"2026-05-22T16:15:23Z","abstract_excerpt":"This paper presents a system design methodology for a large-scale static wireless mesh network for 155 commercial off-the-shelf (COTS) radio nodes at fixed infrastructure sites in a challenging island environment. The architecture consists of approximately ten 15-node clusters, each with designated primary and secondary gateway nodes to support inter-cluster communication. A structured, multi-stage planning methodology was developed to guide network design. Site-specific radio frequency (RF) path loss predictions were generated using Remcom's Wireless InSite ray-tracing platform, incorporating"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.23811","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.23811/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}