{"id":4147,"date":"2025-10-21T15:10:15","date_gmt":"2025-10-21T15:10:15","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4147"},"modified":"2025-10-23T01:32:04","modified_gmt":"2025-10-23T01:32:04","slug":"band-aware-heuristics-as-strong-baselines-for-rf-labeling","status":"publish","type":"post","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4147","title":{"rendered":"Band-Aware Heuristics as Strong Baselines for RF Labeling"},"content":{"rendered":"\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-spectrcyde wp-block-embed-spectrcyde\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"bdVwP5WOGC\"><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4141\">Band-Aware Heuristics as Strong Baselines for RF Labeling<\/a><\/blockquote><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;Band-Aware Heuristics as Strong Baselines for RF Labeling&#8221; &#8212; Spectrcyde\" src=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4141&#038;embed=true#?secret=DX7dDnmagK#?secret=bdVwP5WOGC\" data-secret=\"bdVwP5WOGC\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Introduction<\/h2>\n\n\n\n<p>In a recent paper, Benjamin James Gilbert proposes a simple yet effective set of band-aware labeling heuristics (BAR) for RF datasets. These rules map center frequency and spectral cues to labels like GSM, Wi-Fi, and GPS, providing a clean baseline for machine learning (ML) benchmarks.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Key Highlights<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Transparent Rules<\/strong>: BAR uses frequency-rule sets that are easy to audit and reproduce, serving as a sanity check for dataset curation.<\/li>\n\n\n\n<li><strong>Competitive Performance<\/strong>: Despite its simplicity, BAR achieves a macro F1 score of 0.85, closely rivaling a reference ML classifier&#8217;s 0.90 F1.<\/li>\n\n\n\n<li><strong>Practical Utility<\/strong>: It acts as a strong fallback for underpowered models and helps diagnose issues like class leakage.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Methodology<\/h2>\n\n\n\n<p>The BAR approach relies on a first-hit rule set based on frequency bands and lightweight cues (e.g., bandwidth, hopping behavior). Rules are evaluated in order, with examples including Wi-Fi (2.4-2.5 GHz), GPS L1 (1.575-1.585 GHz), and LTE (700-3800 MHz).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Results<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Overall F1: 0.87<\/li>\n\n\n\n<li>Macro F1: 0.85<\/li>\n\n\n\n<li>Per-class F1 scores: Wi-Fi (0.91), GSM (0.88), GPS (0.93), LTE (0.84), Bluetooth (0.86)<\/li>\n<\/ul>\n\n\n\n<p>These metrics, auto-pulled from a single macros file, ensure consistency across the manuscript.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Takeaways<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>BAR provides a solid, transparent baseline for benchmarking ML across RF bands.<\/li>\n\n\n\n<li>It highlights auditable failure modes, such as errors at band edges or with short dwell hoppers, serving as a curation signal.<\/li>\n\n\n\n<li>The harness-first approach keeps all data in sync, preventing copy-paste errors.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Limitations<\/h2>\n\n\n\n<p>BAR is intentionally simple and band-centric, which can lead to false positives in dense urban settings or miss atypical bandwidths. However, it\u2019s positioned as a baseline and curation tool, not a replacement for ML classifiers.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p>The release of BAR, along with its Python matcher script, offers a valuable resource for the RF community. It\u2019s a stepping stone for more advanced ML models while ensuring transparency and reproducibility.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Introduction In a recent paper, Benjamin James Gilbert proposes a simple yet effective set of band-aware labeling heuristics (BAR) for RF datasets. These rules map center frequency and spectral cues to labels like GSM, Wi-Fi, and GPS, providing a clean baseline for machine learning (ML) benchmarks. Key Highlights Methodology The BAR approach relies on a&hellip;&nbsp;<a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4147\" rel=\"bookmark\"><span class=\"screen-reader-text\">Band-Aware Heuristics as Strong Baselines for RF Labeling<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":4145,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[10],"tags":[],"class_list":["post-4147","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-signal_scythe"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4147","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4147"}],"version-history":[{"count":2,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4147\/revisions"}],"predecessor-version":[{"id":4150,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4147\/revisions\/4150"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/4145"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4147"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4147"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4147"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}